ALBERT

Description: Providing advance warning for impending severe convective weather events (i.e., tornadoes, hail, wind) fundamentally requires an ability to predict and/or detect these hazards and subsequently communicate their potential threat in real time. The National Weather Service (NWS) provides advance warning for severe convective weather through the issuance of tornado and severe thunderstorm warnings, a system that has remained relatively unchanged for approximately the past 65 years. Forecasting a Continuum of Environmental Threats (FACETs) proposes a reinvention of this system, transitioning from a deterministic product-centric paradigm to one based on probabilistic hazard information (PHI) for hazardous weather events. Four years of iterative development and rapid prototyping in the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) with NWS forecasters and partners has yielded insights into this new paradigm by discovering efficient ways to generate, inform, and utilize a continuous flow of information through the development of a human–machine mix. Forecasters conditionally used automated object-based guidance within four levels of automation to issue deterministic products containing PHI. Forecasters accomplished this task in a timely manner while focusing on communication and conveying forecast confidence, elements considered necessary by emergency managers. Observed annual increases in the usage of first-guess probabilistic guidance by forecasters were related to improvements made to the prototyped software, guidance, and techniques. However, increasing usage of automation requires improvements in guidance, data integration, and data visualization to garner trust more effectively. Additional opportunities exist to address limitations in procedures for motion derivation and geospatial mapping of subjective probability.

Description: Professional meteorologists gain a great deal of knowledge through formal education, but two factors require ongoing learning throughout a career: professionals must apply their learning to the specific subdiscipline they practice, and the knowledge and technology they rely on becomes outdated over time. It is thus inherent in professional practice that much of the learning is more or less self-directed. While these principles apply to any aspect of meteorology, this paper applies concepts to weather and climate forecasting, for which a range of resources, from many to few, for learning exist. No matter what the subdiscipline, the responsibility for identifying and pursuing opportunities for professional, lifelong learning falls to the members of the subdiscipline. Thus, it is critical that meteorologists periodically assess their ongoing learning needs and develop the ability to reflectively practice. The construct of self-directed learning and how it has been implemented in similar professions provide visions for how individual meteorologists can pursue—and how the profession can facilitate—the ongoing, self-directed learning efforts of meteorologists.

Description: Rapid-scan weather radars, such as the S-band phased array radar at the National Weather Radar Testbed in Norman, Oklahoma, improve precision in the depiction of severe storm processes. To explore potential impacts of such data on forecaster warning decision making, 12 National Weather Service forecasters participated in a preliminary study with two control conditions: 1) when radar scan time was similar to volume coverage pattern 12 (4.5 min) and 2) when radar scan time was faster (43 s). Under these control conditions, forecasters were paired and worked a tropical tornadic supercell case. Their decision processes were observed and audio was recorded, interactions with data displays were video recorded, and the products were archived. A debriefing was conducted with each of the six teams independently and jointly, to ascertain the forecaster decision-making process. Analysis of these data revealed that teams examining the same data sometimes came to different conclusions about whether and when to warn. Six factors contributing toward these differences were identified: 1) experience, 2) conceptual models, 3) confidence, 4) tolerance of possibly missing a tornado occurrence, 5) perceived threats, and 6) software issues. The three 43-s teams issued six warnings: three verified, two did not verify, and one event was missed. Warning lead times were the following: tornado, 18.6 and 11.5 min, and severe, 6 min. The three tornado warnings issued by the three 4.5-min teams verified, though warning lead times were shorter: 4.6 and 0 min (two teams). In this case, use of rapid-scan data showed the potential to extend warning lead time and improve forecasters’ confidence, compared to standard operations.

Description: Weather forecasting is not an exact science and, in regions near the southern end of the Appalachian Mountains, the vastly different types of topography and frequency of rapidly forming storms can result in high uncertainty in severe weather forecasts. NOAA created its VORTEX-SE research program to tackle these unique challenges and integrate them with social science research to increase the survivability of Southeast U.S. weather. As part of the VORTEX-SE, this study focused on the severe weather preparation and decision making of emergency management, and in particular, how uncertainty in severe weather forecasts impacted the relationship between EMs and weather providers. We conducted in-depth, critical incident background interviews with thirty-five emergency management personnel across fourteen counties. An inductive, data-driven analysis approach revealed several factors contributing to an added layer of practical uncertainty beyond the meteorological forecast uncertainty that impacted and helped explain the nature of trust in the EM-NWS relationship. No- or short-notice events, null events, gaps in information, and differences in perspectives compared to weather forecasters have led emergency managers to modify their procedures in ways that position them to adapt to unexpected changes in the forecast quickly. The need to do so creates a complex, nuanced trust between these groups. This paper explains how EMs developed a nuanced trust of forecast information, how that trust is a recognition of the inherent uncertainty in severe weather forecasts, and how to strengthen the NWS-EM relationship.