Data assimilation of dead fuel moisture observations from remote automated weather stations
Data assimilation of dead fuel moisture observations from remote automated weather stations
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来自远程自动气象站的死燃料水分观测数据同化
DOI：
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发表时间：
2014
期刊：
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通讯作者：
J. Mandel
中科院分区：
文献类型：
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作者：
M. Vejmelka;A. Kochanski;J. Mandel
燃料湿度对荒地火灾的行为有重大影响，是火灾风险评估中的一个重要基本因素。我们提出了一种方法来同化死燃料水分含量（FMC）的观测从远程自动气象站（RAWS）到一个时滞燃料水分模型。RAWS在空间上是稀疏的，需要一种机制来估计可能远离观测站的地点的燃料水分含量。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FMC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间滞后燃料水分模型在耦合的天气-火灾代码WRF-SFIRE的10小时FMC观测从科罗拉多RAWS在2013年。使用留一法测试，我们表明，TSM相比有利的荒地火灾评估系统中使用的平方反比距离插值。最后，我们证明了数据同化方法是能够提高FMC估计在未观测到的燃料类。
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.