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.

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.

Research on the Benefits of REU-Like Programs Undergraduate research is increasingly valued as a critical component of a good undergraduate science education (Halstead, 1997). Through research, it is expected that students will develop a number of behaviors and knowledge, among them an ability to do science, theoretical and practical subject matter knowledge, research and communication skills, independent thought, creativity, and a positive disposition toward the discipline (Kardash, 2000). The involvement of undergraduate students in research is summarized in one of three metaphors: (a) the student as passive learner, where the student is taking courses in subject matter and research methods, (b) the student as a low-skill worker, where the student works as a laboratory technician and is not considered to have the skills needed for significant contributions, and (c) the student as colleague, where the student takes a significant role in many phases of the research process and initiative is rewarded (Kremer and Bringle, 1990). Research suggests that the colleague model of undergraduate research, an essential feature in many undergraduate research programs, has significant benefits for students (Kardash, 2000; Seymour et al, 2004). Most undergraduate research programs follow a four step structure of collaboration that includes (a) identifying and acquiring a disciplinary or interdisciplinary methodology, (b) setting out a concrete investigative problem, (c) carrying out an actual project, and (d) sharing the discovery with peers and the professional community (Dotterer, 2002). Furthermore, Hakim (1998) characterized undergraduate research experiences as resting in four assumptions: (a) that the interaction between the undergraduate research intern and the mentor is focused on student learning, (b) that the research experience will lead to meaningful contributions by the student to the research project, (c) that the procedures and methods used as tools of inquiry by P1.49

The 2012 Phased Array Radar Innovative Sensing Experiment identified how rapidly scanned fullvolumetric data captured known mesoscale processes and impacted tornado-warning lead time. Twelve forecasters from nine National Weather Service forecast offices used this rapid-scan phased-array radar (PAR) data to issue tornado warnings on two low-end tornadic and two nontornadic supercell cases. Verification of the tornadic cases revealed that forecasters' use of PAR data provided a median tornado-warning lead time (TLT) of 20 min. This 20-min TLT exceeded by 6.5 and 9 min, respectively, participants' forecast office and regions' median spring season, low-end TLTs (2008-13). Furthermore, polygon-based probability of detection ranged from 0.75 to 1.0 and probability of false alarm for all four cases ranged from 0.0 to 0.5. Similar performance was observed regardless of prior warning experience. Use of a cognitive task analysis method called the recent case walk-through showed that this performance was due to forecasters' use of rapid volumetric updates. Warning decisions were based upon the intensity, persistence, and important changes in features aloft that are precursors to tornadogenesis. Precursors that triggered forecasters' decisions to warn occurred within one or two typical Weather Surveillance Radar-1988 Doppler (WSR-88D) scans, indicating PAR's temporal sampling better matches the time scale at which these precursors evolve.