  Global maps of the solar photospheric magnetic flux are fundamental drivers
for simulations of the corona and solar wind and therefore are important
predictors of geoeffective events. However, observations of the solar
photosphere are only made intermittently over approximately half of the solar
surface. The Air Force Data Assimilative Photospheric Flux Transport (ADAPT)
model uses localized ensemble Kalman filtering techniques to adjust a set of
photospheric simulations to agree with the available observations. At the same
time, this information is propagated to areas of the simulation that have not
been observed. ADAPT implements a local ensemble transform Kalman filter
(LETKF) to accomplish data assimilation, allowing the covariance structure of
the flux transport model to influence assimilation of photosphere observations
while eliminating spurious correlations between ensemble members arising from a
limited ensemble size. We give a detailed account of the implementation of the
LETKF into ADAPT. Advantages of the LETKF scheme over previously implemented
assimilation methods are highlighted.
