Optimizing Coronal and Solar Wind Model Inputs with Data Assimilation

Details Optimizing Coronal and Solar Wind Model Inputs with Data Assimilation Optimizing Coronal and Solar Wind Model Inputs with Data Assimilation

Dec 2008

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2008agufmsm11b1625r&link_type=abstract

American Geophysical Union, Fall Meeting 2008, abstract #SM11B-1625

Physics

2169 Solar Wind Sources

Scientific paper

The Wang-Sheeley-Arge (WSA) model uses line-of-sight observations of the Sun's surface magnetic field as input to a magnetostatic potential field source surface (PFSS) model of coronal expansion, which is in turn used to determine the ambient solar wind speed and IMF polarity. Despite its relative simplicity, the WSA does a very reasonable job of predicting large-scale, persistent structures in the solar wind. In fact, in the absence of transient events like coronal mass ejections (CMEs), the primary error source for WSA predictions often appears to be related to errors in its solar magnetogram inputs. We employ a data assimilation scheme that augments the usual model state vector with variables that modify the WSA's magnetogram inputs in a manner that minimizes WSA prediction errors when compared to in situ measurements.

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