ESSIC/CISESS student intern Terrence Pierce, a freshman at the University of Maryland, was recently selected as Honorable Mention at the American Meteorological Society (AMS) student oral competition held at the 2023 AMS annual meeting and the 11th Conference on the Meteorological Application of Lightning Data.
The competition included both graduate and undergraduate students, so Pierce being selected as a freshman is especially notable.
Pierce’s work, titled “Developing a Blended Lightning Dataset and its Applications”, discusses the development of a blended lightning dataset that includes the Geostationary Lightning Mappers (GLMs) on both the Geostationary Operational Environmental Satellite (GOES) -16 and -17, National Lightning Detection Network (NLDN), and the Earth Networks Total Lightning Network (ENTLN) over the CONUS region. This blended dataset provides the “ground-truth” for individual lightning networks validation or cross comparison studies over a large area. It also helps to find detection features of both ground and satellite lightning sensors.
The blending algorithms have been implemented into a standalone user-friendly application with a Graphical User Interface (GUI). Users can customize their input datasets, blending parameters, and output type to create their own blended datasets. The output also includes sensor comparison statistics such as detection efficiencies and figures such as plan-view plots. This study will detail the algorithmic process behind blending the datasets and include an application example – using supervised learning models for GLM discharge classification with the blended dataset as the training dataset. Potentially, Pierce and his team will expand the algorithms to include regional datasets such as the Lightning Mapping Array (LMA) and Lightning Imaging Sensor (LIS) to create a better representation of individual flashes and study the detection characteristics of each network in some specific regions.