来自远程自动气象站的死燃料湿度观测的资料同化
Data assimilation of dead fuel moisture observations from remote automated weather stations
摘要：
可燃物含水量对野火的行为有着重要的影响，是火灾风险评估中的一个重要基础因素。我们提出了一种方法来同化死燃料水分含量观测从远程自动气象站（RAWS）到一个时滞燃料水分模型。RAWS在空间上很稀疏，需要一种机制来估计可能远离观测站的位置的燃料水分含量。这是安排使用趋势面模型（TSM），它使我们能够考虑到地形和大气状态对燃料水分含量的空间变异性的影响。在每个感兴趣的位置处，TSM提供伪观测，该伪观测通过卡尔曼滤波被同化。该方法进行了测试与时滞燃料水分模型在耦合天气火灾代码WRF-SFIRE 10小时燃料水分含量观测科罗拉多州RAWS在2013年。我们使用留一法测试的TSM相比，毫不逊色，在荒地火灾评估系统中使用的距离平方反比插值。最后，我们证明了资料同化方法是能够提高燃料水分含量估计在未观察到的燃料类。
英文摘要：
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 (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 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.
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