Scientific Aspects of Forecasting Ionospheric Space Weather

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. Understanding forecasts for the thermosphere and ionosphere is the objective of our effort within the NASA/NSF Partnership for Collaborative Space Weather Modeling. Despite scientific progress over the last few years, there is currently no system in place to forecast moderate to intense storms in Earth's upper atmosphere caused by solar wind disturbances. Forecasting solar wind-driven variability in the ionosphere poses especially stringent tests of our scientific understanding and modeling capabilities, in particular of coupling processes to regions above and below. We will describe our work with community models to develop ionospheric forecasts starting with the solar wind driver. A number of challenges are addressed, including high latitude energy deposition and its impact on global thermospheric circulation patterns and composition. 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. 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 and changing ionospheric conductivities. An important systems-level science question is: 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.