Data assimilation for wildland fires
Data assimilation for wildland fires
复制标题
荒地火灾的数据同化
DOI：
10.1109/mcs.2009.932224
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发表时间：
2007
期刊：
影响因子：
--
通讯作者：
Minjeong Kim
中科院分区：
文献类型：
--
作者：
J. Mandel;J. Beezley;J. Coen;Minjeong Kim
提出了两种荒地火灾模型以及同化这些模型中的数据的方法。 EnKF是在分布式内存高性能计算环境中实现的。数据同化方法是结合 EnKF 与 Tikhonov 正则化来开发的，以避免非物理状态，并结合图像处理的配准和变形思想来允许大的位置校正。即使存在较大修正，数据同化方法也可以跟踪数据，同时避免发散。这些方法可以同化网格数据，但台站数据的同化和数据采集的引导留待未来发展。半经验火蔓延模型通过水平集方法并与 WRF 模型相结合来实现。
Two wildland fire models and methods for assimilating data in those models are presented. The EnKF is implemented ina distributed-memory high-performance computing environment. Data assimilation methods are developed combining EnKF with Tikhonov regularization to avoid nonphysical states and with the ideas of registration and morphing from image processing to allow large position corrections. The data assimilation methods can track the data even in the presence of large corrections, while avoiding divergence. The methods can assimilate gridded data, but the assimilation of station data and steering of data acquisition is left to future developments. A semi-empirical fire spread model is implemented by the level-set method and coupled with the WRF model.