Improving Decision-Making Using Probabilistic Forecasting: A Case Study Using Experimental National Weather Service Snowfall Graphics

In meteorology, there is an emerging trend away from "deterministic," single-solution forecasting without any expressions of confidence to "probabilistic forecasting," in which meteorologists clearly indicate how confident they are in a forecast. With improvements in high performance computing and numerical models, forecasters can now take advantage of bundling numerous solutions for a specific weather event. This capability, known as ensemble forecasting, can provide information that can describe possible ranges in values for a predicted event.

Under contract to the National Weather Service (NWS), Eastern Research Group, Inc. (ERG) used social and behavioral science and risk communication techniques to test and make recommendations for improving a suite of probabilistic, internet-based snowfall forecast graphics currently produced and used by some NWS Weather Forecast Offices (WFOs) in the Southern, Eastern, and Central NWS regions. ERG conducted interviews and focus groups with NWS forecasters and a broad range of decision-makers, including officials from transportation agencies, airports, local public works, emergency management agencies, and schools to determine whether probabilistic snow total forecasts improve decision-making by NWS core partners, including their ability to distinguish between low-impact and high-impact events. ERG also investigated whether there was an optimal mix of textual and visual elements to best convey information to improve impact-based decision support services (IDSS).

The NWS will use this research to consider improvements to the probabilistic snowfall graphics. Findings and recommendations from this project are also intended to feed into broader NWS efforts to communicate uncertainty and impacts across NWS service areas for the protection of life and property.