NSF TTP-P: Translating Lower Ionosphere Electron Density Maps Towards a Market Product

This project is funded through the NSF Translation to Practice (TTP) program, which supports efforts to turn research discoveries into practical tools that benefit communities, industry, and society. Solar storms can knock out the radio communications that aircraft, ships, and emergency responders depend on — sometimes for hours at a time, with serious safety and economic consequences. A key reason these outages are difficult to predict and manage is that a particular layer of the atmosphere that lies at the edge of space, called the D-region, remains largely unmeasured and poorly understood. Sitting roughly 40 to 55 miles above Earth's surface, the D-region is too high for weather balloons and too low for satellites to reach directly. This TTP-P award supports a team at Georgia Tech, in partnership with the weather technology company Vaisala Inc., to build a system for mapping the D-region continuously and in near real time across the entire globe. The team uses radio signals produced by lightning — which strike millions of times each day worldwide — to detect and track conditions in this difficult-to-reach atmospheric layer, in a way that is similar to how doctors use magnetic resonance imaging (MRI) technology to see inside the body. Better knowledge of the D-region will help airline and maritime operators manage radio communication during solar storms, help power grid operators prepare for damaging solar disturbances that can cause blackouts, and strengthen backup navigation and timing systems in case a Global Positioning System (GPS) becomes unreliable. The ionospheric D-region (60–90 kilometer altitude) is the primary absorber of the high-frequency radio signals used by aviation, maritime, military, and emergency communications. It is also the atmospheric layer most directly affected by solar X-ray emissions during space weather events. Despite its importance, real-time D-region monitoring remains limited. A leading operational product — National Oceanic and Atmospheric Administration's D-Region Absorption Prediction (D-RAP) — relies on empirical models with coarse spatial resolution and no direct measurement of electron density. This project advances toward commercialization a D-region tomographic imaging system developed at Georgia Tech, which uses very-low-frequency (VLF) radio signals from both natural lightning and ground-based radio transmitters to produce spatially resolved, three-dimensional maps of D-region electron density. The core innovation is a physics-constrained inversion algorithm — drawing on techniques from medical imaging — that fuses these two complementary observation types into global maps that can be updated on minutes time scale, a horizontal resolution of a few hundred kilometers. The research and translational activities are carried out in partnership with Vaisala Inc., whose global lightning detection network already includes dozens of operational VLF receivers distributed worldwide, enabling global coverage without new hardware deployment. Research objectives include reformulating the mapping algorithm for a spherical Earth geometry, achieving near-real-time operational performance in Vaisala's computing environment, and validating results against other independent measurements. Anticipated outcomes include a validated global D-region mapping prototype with quantified uncertainty at every grid point, a commercialization plan centered on technology licensing to Vaisala, and open algorithmic tools for the scientific community. 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 Award ID: 2553000 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Morris Cohen | Institution: Georgia Tech Research Corporation, ATLANTA, GA | Award Amount: $1,150,000 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2553000 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2553000.html