The Effect of Office Culture and Human Bias in the Tornado Warning Decision Process in the NWS Central Region (100)

Theodore Funk, National Weather Service, Louisville, KY



In late 2016, the National Weather Service (NWS) Central Region (CR) Tornado Warning Improvement Project (TWIP) team sent out a comprehensive volunteer survey to all meteorologists at CR Weather Forecast Offices (WFOs). The survey was designed to provide a detailed understanding of the current state of the CR tornado warning decision process, and included questions and request for feedback on office culture, radar data usage, human factors/bias, risk communication, and training needs. A total of 326 people responded or about 60-65% of CR meteorologists, including 247 forecasters and 79 managers.

Some findings were striking, strongly suggesting an inconsistent warning decision process among meteorologists. For example, varying levels of knowledge and experience make valid tornadogenesis conceptual model training a clear need, especially for quasi-linear convective systems (QLCSs). Effective data interrogation strategies also were desired. Many roadblocks to a reliable decision process center on local office culture, human collaboration, and personal bias/filters. For example, management-forecaster interaction, inconsistency from person-to-person within and between offices, and varying levels of communication all contribute. Fear of missing or overwarning a tornado, and the effect of storm history can play a significant role, differing between forecasters. Office noise and mental/physical fatigue can have substantial and varying impact as well. Confidence changes based on storm mode, and even storm location and time of day/night weighs on decisions.

Understanding how the human, not just the science, affects warning decisions and the protection of life and property is absolutely crucial. This presentation will highlight factors which inherently contribute to an inconsistent tornado warning decision process across CR (and potentially other NWS regions), and discuss key TWIP recommendations to mitigate human and cultural bias in order to facilitate a more objective and science-based warning approach.