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, fals
e 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.
Fuel moisture has a major influence on the behavior of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content observations from remote automated weather stations (RAW
S) into a time-lag fuel moisture model. RAWS are spatially sparse and a mechanism is needed to estimate fuel moisture content at locations potentially distant from observational stations. This is arranged using a trend surface model (TSM), which allows us to account for the effects of topography and atmospheric state on the spatial variability of fuel moisture content. At each location of interest, the TSM provides a pseudo-observation, which is assimilated via Kalman filtering. The method is tested with the time-lag fuel moisture model in the coupled weather-fire code WRF-SFIRE on 10-hr fuel moisture content observations from Colorado RAWS in 2013. We show using leave-one-out testing that the TSM compares favorably with inverse squared distance interpolation as used in the Wildland Fire Assessment System. Finally, we demonstrate that the data assimilation method is able to improve fuel moisture content estimates in unobserved fuel classes.
