Forecasting Daily Wildfire Activity Using Poisson Regression
Forecasting Daily Wildfire Activity Using Poisson Regression
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DOI：
10.1109/tgrs.2020.2968029
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发表时间：
2020-02
影响因子：
8.2
通讯作者：
Casey A. Graff;S. Coffield;Yang Chen;E. Foufoula‐Georgiou;J. Randerson;Padhraic Smyth
中科院分区：
文献类型：
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作者：
Casey A. Graff;S. Coffield;Yang Chen;E. Foufoula‐Georgiou;J. Randerson;Padhraic Smyth
野火及其排放物降低了世界许多地区的空气质量，每年导致数千人过早死亡。烟雾预报系统有可能通过提供未来几天内地面气溶胶浓度（和健康危害）的估计值来改善健康结果。在大多数业务烟雾预报系统中，火灾排放量被假定为在天气预报期间保持不变，并使用卫星观测进行初始化。最近的工作表明，有可能通过预测排放量的时间演变来改进这些模型。在这里，我们使用中分辨率成像光谱仪（MODIS）卫星火灾计数和ERA中期再分析的天气数据开发统计模型来预测未来一到五天的火灾活动。我们的预测框架包括两个泊松回归模型，分别代表新的点火和现有火灾的动态粗分辨率的空间网格。我们使用十年的主动火灾探测在阿拉斯加开发的模型，并使用交叉验证方法来评估模型的性能。我们的研究结果表明，回归方法在预测每日火灾活动方面比基于持续性的模型（由于不考虑火灾熄灭而高估了火灾计数）更准确，蒸汽压赤字作为回归方法中基于天气的单一预测因子特别有效。
Wildfires and their emissions reduce air quality in many regions of the world, contributing to thousands of premature deaths each year. Smoke forecasting systems have the potential to improve health outcomes by providing future estimates of surface aerosol concentrations (and health hazards) over a period of several days. In most operational smoke forecasting systems, fire emissions are assumed to remain constant during the duration of the weather forecast and are initialized using satellite observations. Recent work suggests that it may be possible to improve these models by predicting the temporal evolution of emissions. Here, we develop statistical models to predict fire activity one to five days into the future using Moderate Resolution Imaging Spectroradiometer (MODIS) satellite fire counts and weather data from ERA-interim reanalysis. Our predictive framework consists of two-Poisson regression models that separately represent new ignitions and the dynamics of existing fires on a coarse resolution spatial grid. We use ten years of active fire detections in Alaska to develop the model and use a cross-validation approach to evaluate model performance. Our results show that regression methods are significantly more accurate in predicting daily fire activity than persistence-based models (which suffer from an overestimation of fire counts by not accounting for fire extinction), with vapor pressure deficit being particularly effective as a single weather-based predictor in the regression approach.