Forecasting the Solar Photosphere’s Magnetic Flux with Local Data Assimilation

Monday, 15 December 2014
Kyle S. Hickmann, Los Alamos National Laboratory, Los Alamos, NM, United States, Humberto C Godinez, Los Alamos National Lab, Los Alamos, NM, United States, Carl J Henney, Air Force Research Laboratory Kirtland AFB, Kirtland AFB, NM, United States and Charles Nickolos Arge, AFRL/RVBXS, Kirtland Afb, NM, United States
Accurate forecasts of the photospheric magnetic flux are important since the photosphere provides the driving bound-
ary conditions for the Corona and Solar wind which impact near Earth space weather. These space weather phenomena
effect satellite trajectories and communication systems as well as safety on manned space missions. In this presen-
tation we detail our recent improvements to the data assimilation mechanisms in the Air Force Data Assimilative
Photospheric flux Transport (ADAPT) model. These include implementation of the local ensemble transform Kalman
filter (LETKF) for the assimilation of satellite observations. In the past non-local ensemble methods have been used
to assimilate data into photosphere models. Due to the small ensemble sizes allowed for Solar forecasts spurious
correlations were introduced in the sample covariance, causing model divergence from observations. With our imple-
mentation of the LETKF in ADAPT this ensemble divergence has been reduced. In addition multi-scale techniques
have been implemented in ADAPT to deal with the lack of active region creation in the photosphere model. Lack
of large scale active region creation in the ADAPT model caused ensemble bias when assimilating observations of
newly created regions using ensemble Kalman methods. Separating the scales at which active regions occur allows
observational noise for such regions to be controlled independently. We show that our consideration of the multi-scale
nature of photosphere flux transport has allowed more accurate assimilation of large active regions.