A comparison of statistical downscaling methods suited for wildfire applications
A comparison of statistical downscaling methods suited for wildfire applications
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DOI：
10.1002/joc.2312
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发表时间：
2012-04-01
期刊：
影响因子：
--
通讯作者：
Brown, Timothy J.
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文献类型：
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作者：
Abatzoglou, John T.;Brown, Timothy J.
在野火分析中需要基于地点的数据，特别是在地形不同的地区，这些地区不仅促进了气象变量的强烈梯度，而且还促进了复杂的火灾行为。然而，大多数缩小尺度的方法不适合野火应用，因为缺乏每日时间尺度和对燃料易燃性和火灾蔓延至关重要的变量，如湿度和风。两种统计降尺度方法，每日偏差校正空间降尺度(BCSD)和多变量适应构造相似(MACA)，直接结合全球气候模式的每日数据，在美国西部使用全球再分析数据进行了验证。虽然这两种方法都优于再分析的直接插值法，但MACA在温度、湿度、风和降水方面表现出额外的技巧，这是因为它能够联合降低温度和露点温度，并且使用模拟模式而不是插值法。这两种缩小尺度方法在跟踪火灾危险指数和极端火灾危险时段方面都显示了增值信息；然而，MACA的表现优于每日BCSD，因为它能够更准确地捕捉相对湿度和风。版权所有(C)2011皇家气象学会
Place-based data is required in wildfire analyses, particularly in regions of diverse terrain that foster not only strong gradients in meteorological variables, but also complex fire behaviour. However, a majority of downscaling methods are inappropriate for wildfire application due to the lack of daily timescales and variables such as humidity and winds that are important for fuel flammability and fire spread. Two statistical downscaling methods, the daily Bias corrected Spatial Downscaling (BCSD) and the Multivariate Adapted Constructed Analogs (MACA) that directly incorporate daily data from global climate models, were validated over the western US using global reanalysis data. While both methods outperformed results obtained from direct interpolation from reanalysis, MACA exhibited additional skill in temperature, humidity, wind, and precipitation due to its ability to jointly downscale temperature and dew point temperature, and its use of analog patterns rather than interpolation. Both downscaling methods exhibited value added information in tracking fire danger indices and periods of extreme fire danger; however, MACA outperformed the daily BCSD due to its ability to more accurately capture relative humidity and winds. Copyright (C) 2011 Royal Meteorological Society