Connecting Space Weather Products Using Artificial Neural Network Training

Abstract:

One of the many challenges we face in providing information useful to the operational space weather community is specifying the drivers and the response. In this talk we look at developing a simple and robust method of inferring the solar EUV flux from solar radio emissions. Artificial Neural Network (ANN) training or machine learning has been used to derive the solar EUV (26-34 nm) flux through training with solar radio fluxes at 410, 610, 1415, 2695, 4995, and 8800 MHz. There are still a significant number of users of HF radio wavelengths. They want to be able to monitor the global ionosphere. SSUSI and GUVI and, now, GOLD provide observations of the atomic oxygen emissions at 135.6nm. We use AAN to derive the nightside ionospheric TEC from FUV measurements on nightside. We will discuss the results through a few examples and their application to space weather operations.