Data assimilation of dead fuel moisture observations from remote automated weather stations
Martin Vejmelka A B D , Adam K. Kochanski C and Jan Mandel A BA Department of Mathematical and Statistical Sciences, University of Colorado Denver, PO Box 173363, Denver, CO 80217-3364, USA.
B Institute of Computer Science, Czech Academy of Sciences, Pod Vodárenskou věží 271/2, 182 07 Praha 8, Czech Republic.
C Department of Atmospheric Sciences, University of Utah, William Browning Building, 135S 1460E, Salt Lake City, UT 84112-0102, USA.
D Corresponding author. Email: vejmelkam@gmail.com
International Journal of Wildland Fire 25(5) 558-568 https://doi.org/10.1071/WF14085
Submitted: 15 May 2014 Accepted: 23 March 2015 Published: 19 April 2016
Abstract
Fuel moisture has a major influence on the behaviour of wildland fires and is an important underlying factor in fire risk assessment. We propose a method to assimilate dead fuel moisture content (FMC) observations from remote automated weather stations (RAWS) 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 FMC. 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-h FMC observations from Colorado RAWS in 2013. Using leave-one-out testing we show that the TSM compares favourably 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 on FMC estimates in unobserved fuel classes.
Additional keywords: data assimilation, dead fuel moisture, equilibrium, Kalman filter, remote automated weather stations, time lag model, trend surface model.
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