SA12A-08:
Forecasting Ionospheric Space Weather Due To High Speed Streams
Monday, 15 December 2014: 11:50 AM
Anthony J Mannucci1, Olga P Verkhoglyadova2, Xing Meng1, Bruce T. Tsurutani2, Xiaoqing Pi2, Erin Michelle Lynch3, Surja Sharma3, Aaron J Ridley4, Ward Manchester5, Chunming Wang6 and Gary Rosen6, (1)Jet Propulsion Laboratory, Pasadena, CA, United States, (2)NASA Jet Propulsion Laboratory, Pasadena, CA, United States, (3)University of Maryland College Park, Montgomery Village, MD, United States, (4)Univ Michigan, Ann Arbor, MI, United States, (5)University of Michigan, Ann Arbor, MI, United States, (6)University of Southern California, Los Angeles, CA, United States
Abstract:
The development of quantitative models that describe physical processes from the Solar corona to the Earth’s upper atmosphere opens the possibility of numerical space weather forecasting with a lead time of a few days. We will describe our work with community models to develop ionospheric forecasts starting with the solar wind driver. Our current focus is the daytime ionospheric response to high-speed solar wind streams that are prevalent during the declining phase of the solar cycle. A number of challenges are addressed, including high latitude energy deposition and its impact on global thermospheric circulation patterns and composition. The degree to which forecasts are successful depends on the manner in which Alfvenic solar wind variability drives the ionospheric response. Large-scale and small-scale magnetospheric processes are important to consider, including the role of particle precipitation in depositing energy into the thermosphere and in changing ionospheric conductivities through increased ionization. Accurate forecasts require that we address the following question: what are the impacts of (less predictable) small-scale processes in determining the large-scale daytime ionospheric response, versus the role of larger scale magnetospheric processes that may be easier to predict? We will compare model-based forecasts with a variety of satellite and ground-based data sources to assess the fidelity of physical processes represented in the models. Outstanding science questions that are relevant to forecasts are described.
