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arXiv · 1309.0159

Data assimilation of fuel moisture in WRF-SFIRE

Martin Vejmelka·Adam K. Kochanski·Jan Mandel
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Abstract

Fuel moisture is a major influence on the behavior of wildland fires and an important underlying factor in fire risk. We present a method to assimilate spatially sparse fuel moisture observations from remote automatic weather stations (RAWS) into the moisture model in WRF-SFIRE. WRF-SFIRE is a coupled atmospheric and fire behavior model which simulates the evolution of fuel moisture in idealized fuel species based on atmospheric state. The proposed method uses a modified trend surface model to estimate the fuel moisture field and its uncertainty based on currently available observations. At each grid point of WRF-SFIRE, this information is combined with the model forecast using a nonlinear Kalman filter, leading to an updated estimate of fuel moisture. We demonstrate the effectiveness of the method with tests in two real-world situations: a region in Southern California, where two large Santa Ana fires occurred recently, and on a domain enclosing Colorado.

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Martin Vejmelka, Adam K. Kochanski, Jan Mandel. 2013-08-31. Data assimilation of fuel moisture in WRF-SFIRE. https://arxiv.org/abs/1309.0159

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