How Meteorologists Learn to Forecast the Weather: Social Dimensions of Complex Learning

Abstract

Weather and climate persistently affect individuals, corporations, and governments, sometimes in significant ways: a poor forecast leaves people unprepared to prevent damage or deal with disruptions to their daily routines, and studies show anywhere from 3.4-25% of the US economy is sensitive to weather. Despite the intangible and tangible significance of good forecasts, weather forecasting is rarely explicitly taught and there is little written about how meteorologists learn to forecast the weather. Literature within meteorology is scant; mainly descriptive. The few empirical studies of professional forecasters addressed the nature of the warning task, forecaster decision making, and forecaster performance, revealing the complexity of the domain without explaining how forecasters are learning. In education and other literature, several constructs may apply, including expertise, learning through reflection, and self-directed learning, but none of these have matured to the level of theory. There is currently no single, comprehensive theory for learning that describes how and why someone would learn to take a body of knowledge and apply it in non-linear ways to real world problems.

This study therefore takes a grounded theory approach, aiming to identify the elements and relationships characteristic of a theory of how meteorologists learn to forecast the weather. Interviews with 11 forecasters resulted in two models. Participants were from two employment sectors, had forecasted several types of weather, and had a range of time in service. The first model describes the triggers for learning and how those change over time. The second describes how forecasters built their ability to forecast the weather. A central, repeating theme about a strong sense of professional identity with their role as a forecaster was consistently important to how the participants engaged in learning, particularly when they were poorly supported and had to create strategies to learn. A second strong theme emerged: learning was faster, forecasters were happier, and their resulting knowledge was better connected and more thorough if participants had good social support. Results are well supported through triangulation with the experiences and observations of training officers, empirical studies and published reflections of forecasters, empirical models of adult learning, and indigenous science learning.