HamSCI Member Wins Undergraduate Research Grant

HamSCI Member Wins Undergraduate Research Grant

Thursday, April 27, 2017 - 14:18

HamSCI member Joshua D. Katz, KD2JAO, was recently announced as a winner of the 2017 NJIT Provost Undergraduate Summer Research award for his research proposal entitled Estimating Ionospheric Parameters Using Real-Time Data Sources. This $3000 grant will allow Mr. Katz to conduct this research at the New Jersey Institute of Technology Center for Solar Terrestrial Research during the upcoming summer, where he develops software solutions to computationally intensive physics problems in the domains of simulation and big data analysis. Mr. Katz's summer research will be supervised by Dr. Nathaniel Frissell, W2NAF, a researcher in the NJIT-CSTR. The proposal abstract is listed below.

Estimating Ionospheric Parameters Using Real-Time Data Sources

Submitted by: Joshua D. Katz, KD2JAO
Research advisor: Nathaniel A. Frissell, W2NAF

Abstract: The ionosphere is a region of the atmosphere that affects communication, radio propagation, and global navigation systems. This makes understanding and modeling ionospheric structures and dynamics important. Modeling this in real time is difficult because there is a limited set of data sources that can be sampled continuously and in real time. We propose an assimilation process that will enable the incorporation of novel data sources, generated by citizen-scientist activities, into real-time modeling and prediction systems. We will use this data source to generalize a method for calibrating ionospheric models to fit conditions currently being experienced by real radio operators. These data sources are large, historical, and provides a unique coverage that is otherwise absent in traditional sounding technologies. Additionally, these data sources are available in real-time. We propose the development and implementation of an algorithm to best-fit a well-accepted climatological ionospheric model to observations of amateur radio communications. Residuals from the data-model comparison will be used to identify periods of abnormal radio propagation that can then be used in future scientific studies.