James Jones, Northrop Grumman, Sean Sanders, Northrop Grumman, Bryan Davis, Air Force Space Command, Space and Missile Systems Center, Remote Sensing Systems Directorate, Cecilia Hedrick, ATECH, Elizabeth J. Mitchell, Johns Hopkins University, Applied Physics Laboratory, Jeffery M. Cox, The Aerospace Corporation
Keywords: Aurora, magnetosphere, radar, clutter, UHF, research, operations, transition, agile
Abstract:
Aurorae are generally caused by collisions of high-energy precipitating electrons and neutral molecules in Earth’s polar atmosphere. The electrons, originating in Earth’s magnetosphere, collide with oxygen and nitrogen molecules driving them to an excited state. As the molecules return to their normal state, a photon is released resulting in the aurora. Aurora can become troublesome for operations of UHF and L-Band radars since these radio frequencies can be scattered by these abundant free electrons and excited molecules. The presence of aurorae under some conditions can lead to radar clutter or false targets. It is important to know the state of the aurora and when radar clutter is likely. For this reason, models of the aurora have been developed and used in an operational center for many decades. Recently, a data-driven auroral precipitation model was integrated into the DoD operational center for space weather. The auroral precipitation model is data-driven in a sense that solar wind observations from the Lagrangian point L1 are used to drive a statistical model of Earth’s aurorae to provide nowcasts and short-duration forecasts of auroral activity. The project began with a laboratory-grade prototype and an algorithm theoretical basis document, then through a tailored Agile development process, deployed operational-grade code to a DoD operational center. The Agile development process promotes adaptive planning, evolutionary development, early delivery, continuous improvement, regular collaboration with the customer, and encourages rapid and flexible response to customer-driven changes. The result was an operational capability that met customer expectations for reliability, security, and scientific accuracy. Details of the model and the process of operational integration are discussed as well as lessons learned to improve performance on future projects.
Date of Conference: September 19-22, 2017
Track: Poster