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Aiming to improve severe weather forecasting and warning lead-times associated with front range thunderstorms over northeastern Colorado, the University of Hawaiʻi at Mānoa School of Ocean and Earth Science and Technology (SOEST) and Jonathan Merage Foundation have expanded their partnership.
Improvements in Colorado’s thunderstorm forecasting rely on innovative data from its Lightning Mapping Array (LMA) network. The network is comprised of 12 stations north of Denver that monitor lightning activity. LMA sensors have revealed distinct tornado signatures 30 minutes prior to the formation of a tornado and are used to predict severe storms that also produce strong straight-line winds and large hail.
The southernmost LMA sensor is currently located 25 miles north of Denver. The new gift will enable the construction and installation of six additional sensor stations around and south of Denver, expanding the LMA network to cover the Denver Metro Area and improve severe weather forecasting for the most densely-populated area of Colorado.
“Not only will this project allow us to provide better information to the Colorado community about incoming and potential severe thunderstorms,” said Professor Steven Businger, chair of the Atmospheric Science Department in SOEST and project lead, “but it will allow scientists to study and refine relationships between lightning information and the tornadic potential of thunderstorms. It will allow us to better predict dangerous storms and improve lead-times for tornado warnings, which has the potential to save lives.”
Two new sensors will be installed this year and four additional sensors will be installed over the next two years.
Partnership also expanding tropical cyclone research
In addition to the new LMA collaboration, the Jonathan Merage Foundation has funded another year of investigation into long-range lightning data. The project is funding a postdoctoral student in Businger’s lab.
“Last year we developed a tropical storm model that can assimilate lightning data,” said Businger. “This year we aim to improve the way cloud processes are handled in the model and run some case studies, such as Hurricane Patricia and Typhoon Haiyan, through the model. This year will get us closer to our goal of improving our ability to predict the track and intensity of tropical cyclones.”
Both projects are currently underway.