Evaluation of Seasonal Forecasts for the Fire Season in Interior Alaska
Evaluation of Seasonal Forecasts for the Fire Season in Interior Alaska
复制标题
阿拉斯加内陆火灾季节的季节预报评估
DOI：
10.1175/waf-d-19-0225.1
复制
发表时间：
2021
影响因子：
2.9
通讯作者：
Sampath, Akila
中科院分区：
文献类型：
--
作者：
Sampath, Akila
在这项研究中，来自国家环境预测中心(NCEP)气候预测系统第2版(CFSv2)的季节预报与观测站的观测结果进行了比较，以评估它们在为阿拉斯加内陆火灾季节产生准确的积聚指数(BUI)预报方面的有效性。这些比较表明，由于负温度和正降水偏差，CFSv2 6-7-8月(JJA)气候学(1994-2017)产生了负偏差的BUI预测。经分位数映射(QM)校正后，温度和降水预报与观测值吻合较好。使用QM修正后的预测计算，长期JJA平均BUI从12提高到42。使用四分位分类方法对QM修正后的BUI预报进行进一步后处理，显示出2004年火季的异常高值，就野火烧毁的面积而言，这是有记录以来最糟糕的。这些结果表明，QM修正后的CFSv2预报可以用于极端火灾事件的预报。对分类的BUI集合成员在次季节尺度上的评估表明，持续出现的BUI预报在累计干旱季节超过150，可用作即将到来的季节将发生极端火灾事件的指标。这项研究证明了QM修正后的CFSv2预报能够提前预测潜在的火季。因此，这些信息可以帮助消防管理人员进行资源分配和备灾。
In this study, seasonal forecasts from the National Centers for Environmental Prediction (NCEP) Climate Forecast System, version 2 (CFSv2), are compared with station observations to assess their usefulness in producing accurate buildup index (BUI) forecasts for the fire season in Interior Alaska. These comparisons indicate that the CFSv2 June–July–August (JJA) climatology (1994–2017) produces negatively biased BUI forecasts because of negative temperature and positive precipitation biases. With quantile mapping (QM) correction, the temperature and precipitation forecasts better match the observations. The long-term JJA mean BUI improves from 12 to 42 when computed using the QM-corrected forecasts. Further postprocessing of the QM-corrected BUI forecasts using the quartile classification method shows anomalously high values for the 2004 fire season, which was the worst on record in terms of the area burned by wildfires. These results suggest that the QM-corrected CFSv2 forecasts can be used to predict extreme fire events. An assessment of the classified BUI ensemble members at the subseasonal scale shows that persistently occurring BUI forecasts exceeding 150 in the cumulative drought season can be used as an indicator that extreme fire events will occur during the upcoming season. This study demonstrates the ability of QM-corrected CFSv2 forecasts to predict the potential fire season in advance. This information could, therefore, assist fire managers in resource allocation and disaster response preparedness.
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