Cloudy with a Chance of Partnership: How Weather Researchers Shape Science to Support Collaboration

In atmospheric science, research on “communication” has often focused on improving the accuracy of consumers' interpretation of weather forecasts and severe weather warnings. Although addressing the forecaster-consumer relationship is important, this type of interaction characterizes only a portion of the communication involved in producing and operating numerical weather prediction (NWP) models. It is equally important that the researchers who build those models are able to communicate ideas with one another and their collaborators.

Research teams that build NWP models are frequently comprised of individuals who differ greatly in expertise and specialty. To be successful, scientists and engineers must effectively communicate with each other, combining their diverse expertise to build a model that accurately represents and predicts atmospheric phenomena. Existing research suggests that communicating across boundaries of expertise is no trivial task, and often requires specific skills (e.g. Collins & Evans, 2007; Contractor & Monge, 2002; Galison, 1997).

This type of communication is particularly important when producing applied NWP models, those intended to inform decision-making in specific operational contexts. In applied NWP, researchers must often collaborate with individuals who have little understanding for how NWP models operate (e.g. groups from outside organizations, end-users, representatives of funding agencies). Successfully communicating with these collaborators is necessary to secure funding, obtain data, and create models that will advance science while informing operational decision-making.

In this talk I will discuss how developing an understanding about the communication occurring among researchers and their collaborators can benefit atmospheric scientists. I draw on a year-long study of the communication occurring in four research teams, each producing different types of applied NWP models, to address the following questions: What communicative strategies do researchers use to effectively collaborate with individuals who have different expertise from their own? And, how do these strategies affect the scientific process?

Across teams in the study, researchers employed complex strategies to ensure successful collaboration. Expanding on existing communication literature, which focuses predominantly on the moment when collaborators explain ideas to each other (e.g. Bechky, 2003; Beck & Keyton, 2009; Brandon & Hollingshead, 2004), I found that researchers' strategies pervaded the scientific process. Researchers were highly strategic when writing proposals, choosing algorithms to include in their models, defining model outputs, and preparing presentations to give to their collaborators. In short, weather researchers often made “scientific” decisions about building their models for communicative reasons.

These findings suggest that doing applied science requires more than traditional scientific knowledge. To be successful, researchers must also be skilled communicators – constantly keeping collaborators in mind and strategically performing science in a manner that not only satisfies their partners, but produces findings that push the boundaries of atmospheric science.

To conclude, I will discuss implications for organizational policy and scientific practice. From a policy perspective, this study highlights the importance of recognizing communicative skills as a unique and paramount component of the scientific collaboration process. From a practice perspective, I will identify several practices that scientists might focus upon if they desire to successfully engage in applied scientific work.

Work Cited

Bechky, B. (2003). Sharing meaning across occupational communities: The transformation of understanding on the production floor. Organization Science, 17, 99-120.

Beck, S. J., & Keyton, J. (2009). Perceiving Strategic Meeting Interaction. Small Group Research, 40(2), 223-246.

Brandon, D. P., & Hollingshead, A. B. (2004). Transactive Memory Systems in Organizations: Matching Tasks, Expertise, and People. Organization Science, 15(6), 633-644.

Collins, H. M., & Evans, R. (2007). Rethinking Expertise. Chicago, IL: University of Chicago Press.

Contractor, N. S., & Monge, P. R. (2002). Managing Knowledge Networks. Management Communication Quarterly, 16, 249-258.

Galison, P. (1997). Image & Logic: A Material Culture of Microphysics. Chicago, IL: University of Chicago Press.