Quantifying contributions of external drivers to Global Ionospheric Map variability

Abstract:

To advance our understanding of the Global Ionospheric Map (GIM) in response to various external drivers, we conduct a systematic and quantitative study of the GIM variability due to space weather conditions and lower atmospheric forcing using entropy-based information theory. The study utilizes over-a-decade of GIM data, F10.7 and solar wind data, as well as lower atmospheric state data. By applying mutual information and transfer entropy, we are able to quantify the information transfer from space weather and lower atmospheric drivers to the GIM and determine the relative importance of each driver on the GIM. In addition, we analyze the predictability properties of the GIM by ranking the external drivers based on the amount of information transfer to the GIM. Our work provides insight into the ionospheric total electron content forecast by identifying key external drivers to include in forecast models.