How meteorologists learn to forecast the weather

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.

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. Thus far 7 of 11 interviews with forecasters, ranging in forecaster work experience from 0.75 to 18 years, have been analyzed. Of the first seven participants, two were employed in unique private sector companies, and five were in various forecasting roles in the National Weather Service. One had been employed in both sectors. Diverse geographical locations weather types are represented. Interviews were analyzed using open, axial, and process coding; structure and context analyses; and diagramming to identify how and why they learn.

Preliminary results show that forecasters first learn simple associations and procedures. Then, to the extent they are able to make connections between theory and the weather they forecast, their understanding becomes increasingly complex and nuanced. The knowledge built through modules and other training remains largely disconnected until social interactions with knowledgeable forecasters—including reading forecast and listserv discussions—and reflection on their forecasts connect that knowledge. The critical link between social and cognitive aspects of learning the complex task of forecasting is clear in this study. Further, this study adds a third dimension: Forecasters with the strongest senses of identity as forecasters, and deepest desires to fill the forecaster role to outside users, appear to have the deepest engagement and understanding of how to forecast. These results will be tested on the remaining four interviews and three pilot interviews before deciding whether a return to the field is warranted to fill in the emerging theory.