  The current fuel moisture content (FMC) subsystems in WRF-SFIRE and its
workflow system WRFx use a time-lag differential equation model with
assimilation of data from FMC sensors on Remote Automated Weather Stations
(RAWS) by the extended augmented Kalman filter. But the quality of the result
is constrained by the limitations of the model and of the Kalman filter. We
observe that the data flow in a system consisting of a model and the Kalman
filter can be interpreted to be the same as the data flow in a recurrent neural
network (RNN). Thus, instead of building more sophisticated models and data
assimilation methods, we want to train a RNN to approximate the dynamics of the
response of the FMC sensor to a time series of environmental data. Because
standard AI approaches did not converge to reasonable solutions, we pre-train
the RNN with special initial weights devised to turn it into a numerical solver
of the differential equation. We then allow the AI training machinery to
optimize the RNN weights to fit the data better. We illustrate the method on an
example of a time series of 10h-FMC from RAWS and weather data from the
Real-Time Mesoscale Analysis (RTMA).
