341 Proficiency Scaling of Warning Forecasters

Monday, 7 January 2013
Exhibit Hall 3 (Austin Convention Center)
Daphne LaDue, CAPS/Univ. of Oklahoma, Norman, OK; and P. L. Heinselman and R. Hoffman

Handout (340.8 kB)

The 2012 Phased Array Innovative Sensing Experiment (PARISE) provided a unique opportunity to study the warning strategies of 12 National Weather Service forecasters (experience ranged from 2–18 years). Probe questions documented their work processes, reasoning, and judgments scan-by-scan through each displaced real-time weather case. In addition, this project investigated the matter of proficiency scaling, that is, determining the experience background that leads individuals to achieve forecasting expertise. This relied on a number of methods of cognitive task analysis (detailed Crandall, Klein and Hoffman, 2006). This report focuses on results from the proficiency scaling.

A Career History protocol documented their early interest, education in and beyond formal schooling, career steps, and duties of their current position. The Recent Case Walkthrough guided the forecaster in discussing recently-experienced and difficult forecasting cases. This structured interview focused first on their work strategies for tornado warning situations and ways they have deviated from their normal or taught strategies. It then asked questions that revealed insights that have allowed them to achieve high levels of proficiency in general. Analysis of the first eight forecasters revealed that they shared many of the same strategic elements of the warning process; key work strategies included maintaining environmental situational awareness and awareness of all regional storms. They were also concordant in defining what made a storm easy to warn on, versus increasingly difficult. A wide range of examples came to mind regarding how they had adapted work processes and had moments of insight. Individual differences in forecasting and warning strategy will be discussed; for example, less experienced forecasters provided examples where they had identified deviations from normal conditions, but did not have a conceptual model underlying their decisions. This finding fits with results from previous studies of forecaster reasoning as a function of experience and proficiency level (e.g., Pliske et al., 1997; Hoffman et al., 2006).

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