  Currently available satellite active fire detection products from the VIIRS
and MODIS instruments on polar-orbiting satellites produce detection squares in
arbitrary locations. There is no global fire/no fire map, no detection under
cloud cover, false negatives are common, and the detection squares are much
coarser than the resolution of a fire behavior model. Consequently, current
active fire satellite detection products should be used to improve fire
modeling in a statistical sense only, rather than as a direct input. We
describe a new data assimilation method for active fire detection, based on a
modification of the fire arrival time to simultaneously minimize the difference
from the forecast fire arrival time and maximize the likelihood of the fire
detection data. This method is inspired by contour detection methods used in
computer vision, and it can be cast as a Bayesian inverse problem technique, or
a generalized Tikhonov regularization. After the new fire arrival time on the
whole simulation domain is found, the model can be re-run from a time in the
past using the new fire arrival time to generate the heat fluxes and to spin up
the atmospheric model until the satellite overpass time, when the coupled
simulation continues from the modified state.
