What is KPHI TV ?
KPHI TV is the fictitious television news station affiliated with research in the Probabilistic Hazard Information (PHI) project in NOAA’s Hazardous Weather Testbed. The KPHI TV Weather Team is comprised of broadcast meteorologists that participate in the HWT PHI project. Participants are recruited nationwide from all market sizes. Broadcast participants perform typical job functions under a simulated television studio environment as they receive experimental probabilistic information and severe weather warnings. NOAA researchers study how broadcast meteorologists interpret, use, and communicate probabilistic information. Decision points of interest include when to run crawls, post to social media, interrupt commercials, and interrupt programming. Researchers use results from the data collected in the KPHI TV studio to help shape the future of the National Weather Service warning paradigm.
Station Management (NOAA Researchers)
Holly Obermeier, KPHI TV Co-News Director
Holly is an associate scientist for the University of Colorado-Boulder Cooperative Institute for Research in Environmental Sciences (CIRES) and the Earth Systems Research Laboratory (ESRL). Her areas of research include exploring the use and communication of probabilistic hazard information (PHI) in weather warnings, and experimenting with PHI output from the Hazard Services dissemination framework. She has worked with both NWS forecasters and end-users (primarily broadcast meteorologists) under the Hazardous Weather Testbed since 2015. Holly formerly worked as a researcher with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma. Before entering the world of research, Holly was an on-air broadcast meteorologist. She worked extensively in severe weather markets, including KLBK in Lubbock, TX and KETV in Omaha, NE. Holly completed her undergraduate and graduate education at the University of Nebraska-Lincoln.
Kodi Berry, KPHI TV Co-News Director:
Kodi is the Program Lead for Forecasting a Continuum of Environmental Threats (FACETs) at the National Severe Storms Laboratory (NSSL). Her primary area of research includes how broadcast meteorologists use and communicate probabilistic information in a mock television studio environment. Before joining NSSL, Kodi was the Sea Grant Liaison to NSSL and the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma. She also managed and coordinated across all experiments that took place in the NOAA’s Hazardous Weather Testbed. Kodi completed her undergraduate education at the University of Nebraska-Lincoln and graduate education at the University of Oklahoma.
Kim Klockow-McClain, KPHI TV Research Consultant:
Kim is a research scientist and Societal Applications Coordinator with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma and the National Severe Storms Laboratory (NSSL). Her research involves behavioral science focused on weather and climate risk, and specifically explores the effects of risk visualization on judgment, and perceptions of severe weather risk from place-based and cognitive perspectives. Before joining CIMMS/NSSL, Kim was a UCAR Postdoctoral Researcher and Policy Advisor at the NOAA OAR Office of Weather and Air Quality. Kim completed her undergraduate education at Purdue University and graduate education at the University of Oklahoma.
Adrian Campbell, KPHI TV Co-Chief Engineer:
Adrian is a research scientist with the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) at the University of Oklahoma and the National Severe Storms Laboratory (NSSL). He actively develops the Probabilistic Hazard Information (PHI) Prototype Tool and supports data flow and display in PHI Prototype Experiments. The focus of his research is on the perception and interpretation of image content with application to storm segmentation and tracking algorithms. An additional, related research interest is virtual reality environments that are capable of visualizing complex datasets and their potential contributions to data exploration and analysis.