SBIR Phase I: Extremely Low Frequency Characterization of High-Risk Lightning

Award ID: 2223166

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project includes a notable reduction in the area burned by lightning-initiated wildfires. Understanding which lightning strikes are capable of igniting wildfires is critical as in the Western U.S. lightning-initiated wildfires are responsible for over 70% of the area burned in these environmental catastrophes. Globally, wildfires are responsible for 6.45 gigatons of carbon dioxide (CO2) emissions annually (18% of total emissions). Detecting high risk lightning strikes (those capable of igniting wildfires) may also significantly reduce losses of life, wildlife, habitats, property, and forests as currently lightning-initiated wildfires in the US devastate 4-6 million acres per year. The reduction of wildfires can reduce large evacuations and smoke-related health conditions, thereby improving the health and welfare of the American public. Both people and businesses would benefit from lower insurance rates due to the decreased risk of wildfire damage. Large wildfires are a constant concern to more than half of the mission assurance priority military installations due to routine testing and training activities that are significant ignition sources. The proposed project may also address military ignition concerns. Wildfires start when a long continuing current (LCC) strikes the ground at a location where the environmental conditions are conducive for fire ignition. LCCs are those that last for 40 ms or longer and are essentially responsible for excessive heating. The transformative aspect of this research lies in the ground-based characterization of Extremely Low Frequency (ELF) lightning emissions to identify LCC strikes, with a 95% target detection efficiency and with 40 m accuracy. While for most lightning strikes the current ceases to flow after tens of microseconds, a small portion of lightning strikes (less than 10%) contain a continuing current that lasts thousands of times longer, from tens to hundreds of milliseconds. This can be viewed as a quasi-stationary arc between the cloud charge source and the ground and is detectable through electrostatic field changes and ELF emissions. A secondary innovative feature lies in the use of machine learning algorithms to pinpoint high risk lightning ignitions by analyzing the environmental conditions at the LCC strike location. This technology can identify a fire in seconds, unlike the present heat or smoke identification systems that can take hours or days to identify a fire. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

NSF Program Director: Rajesh Mehta