Forecasting Frontiers Part I: Solar Drivers of Space Weather Observations

Details Forecasting Frontiers Part I: Solar Drivers of Space Weather Observations Forecasting Frontiers Part I: Solar Drivers of Space Weather Observations

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsa51a1532j&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SA51A-1532

Physics

2101 Coronal Mass Ejections (7513), 2139 Interplanetary Shocks, 2164 Solar Wind Plasma, 2169 Solar Wind Sources

Scientific paper

The new AFRL Space Weather Forecasting Laboratory (SWFL) seeks to understand, explore and research the elements involved in developing a successful program of space situational awareness. Originating with the primary solar drivers, space weather envelops a continuum of critically connected heliospheric, ionospheric and thermospheric regimes. Within each regime, a successful space weather awareness and forecast situation requires a multi- pronged effort that spans areas of reliable monitoring, data acquisition and its timely availability, fusing of the data with physical, heuristic and numerical models, and timely now-cast and forecast abilities. In this presentation we will address solar drivers. We will illustrate the need for monitoring solar surface phenomena. Within the realm of solar drivers, eruptive solar activity comprises of primarily flares and mass ejections, which are, in turn, driven by local physical conditions of constantly competing magnetic, hydrodynamic and thermodynamic forces. These physical conditions span the entire solar atmosphere from below the visible solar photosphere through chromosphere to corona. We will address the need for timely monitoring of physical conditions leading to these phenomena and the diagnostic potential of various seemingly heterogeneous physical quantities connected to the resultant eruptive activity. A discussion of time-scales of phenomena, and resources/tools required for timely monitoring, cadence, tolerances to latency in data availability, testing/evaluation of physical and data models and the viability of a deterministic now-cast and forecast models will be covered.

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