Biomass burning releases a vast amount of aerosols into the atmosphere, often leading to severe air quality and health problems. Prediction of the air quality effects from biomass burning emissions is challenging due to uncertainties in fire emission, plume rise calculation, and other model inputs/processes. Ensemble forecasting is increasingly used to represent model uncertainties. In this paper, an ensemble forecast was conducted to predict surface PM_2.5 during the 2018 California Camp Fire event using the National Oceanic and Atmospheric Administration (NOAA) HYSPLIT dispersion model at 0.1° horizontal resolution. Different combinations of four satellite‐based fire emission data sets (FEER, FLAMBE, GBBEPx, and GFAS), two plume rise schemes (Briggs and Sofiev), various meteorology inputs, and model setup options were used to create the forecast ensemble, for a total of 112 experiments. The performance of each ensemble member and the ensemble mean were evaluated using ground‐based observations, with four statistical metrics and an overall rank. The ensemble spread of the 112 members reached 1,000 μg/m³, highlighting the large uncertainty in wildfire forecast. The ensemble mean displayed the best performance. Each fire emission product contributed to one or more members among the top 10 performers, revealing the forecasting dependence on both the quality of fire emissions data and model representation of emission, transport, and removal processes. In addition, an ensemble size reduction technique was introduced. With the help of this technique, the ensemble size was reduced from 112 to 28 members and still produced an ensemble mean that yielded comparable or even better performance to that of the full ensemble.

中文翻译：

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使用HYSPLIT传输和扩散模型对2018年营火事件进行PM2.5预报

生物质燃烧将大量气溶胶释放到大气中，经常导致严重的空气质量和健康问题。由于火灾排放，羽流上升计算和其他模型输入/过程的不确定性，对生物质燃烧排放对空气质量的影响进行预测具有挑战性。集成预测越来越多地用于表示模型的不确定性。在本文中，进行了整体预报以预报表面PM _2.5使用国家海洋和大气管理局（NOAA）HYSPLIT散射模型在0.1°水平分辨率下进行的2018年加利福尼亚营火事件中。四个基于卫星的火灾发射数据集（FEER，FLAMBE，GBBEPx和GFAS），两个羽状上升方案（Briggs和Sofiev），不同的气象学输入和模型设置选项的不同组合用于创建预报集合。总共112个实验。使用基于地面的观测值，四个统计指标和一个整体排名对每个合奏成员的表现和合奏平均值进行评估。112名成员的总体传播达到1,000μg/ m ³，突显了野火预报的巨大不确定性。合奏平均值显示最佳性能。每个火灾排放产品都贡献了表现最出色的10个成员中的一个或多个成员，从而揭示了预测对火灾排放数据质量以及排放，运输和清除过程的模型表示的依赖。另外，引入了整体尺寸减小技术。借助此技术，合奏的大小从112个减少到28个成员，并且仍然产生了合奏的均值，可以产生与整个合奏相当或什至更好的性能。