This study examines the benefit of using a dynamical ensemble for 48 hr deterministic and probabilistic predictions of near‐surface fine particulate matter (PM_2.5) over the contiguous United States (CONUS). Our ensemble design captures three key sources of uncertainties in PM_2.5 modeling including meteorology, emissions, and secondary organic aerosol (SOA) formation. Twenty‐four ensemble members were simulated using the Community Multiscale Air Quality (CMAQ) model during January, April, July, and October 2016. The raw ensemble mean performed better than most of the ensemble members but underestimated the observed PM_2.5 over the CONUS with the largest underestimation over the western CONUS owing to negative PM_2.5 bias in nearly all the members. To improve the ensemble performance, we calibrated the raw ensemble using model output statistics (MOS) and variance deficit methods. The calibrated ensemble captured the diurnal and day‐to‐day variability in observed PM_2.5 very well and exhibited almost zero mean bias. The mean bias in the calibrated ensemble was reduced by 90–100% in the western CONUS and by 40–100% in other parts of the CONUS, compared to the raw ensemble in all months. The corresponding reduction in root‐mean‐square error (RMSE) was 13–40%. The calibrated ensemble also showed 30% improvement in the RMSE and spread matching compared to the raw ensemble. We have also shown that a nine‐member ensemble based on combinations of three meteorological and three anthropogenic emission scenarios can be used as a smaller subset of the full ensemble when sufficient computational resources are not available in the operational setting.

中文翻译：

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一种新颖的整体设计，用于对连续美国（CONUS）上的细颗粒物进行概率预测

这项研究检验了使用动态集合对连续的美国（CONUS）进行近48小时的确定性和概率性预测的近地表细颗粒物（PM _2.5）的好处。我们的整体设计捕获了PM _2.5建模过程中三个主要的不确定性来源，包括气象，排放和二次有机气溶胶（SOA）的形成。在2016年1月，4月，7月和10月期间，使用社区多尺度空气质量（CMAQ）模型对24个集合成员进行了模拟。原始集合均值的表现优于大多数集合成员，但低估了观察到的CONUS的PM _2.5由于PM _2.5负值，西部CONUS的最大低估几乎所有成员都有偏见。为了提高整体性能，我们使用模型输出统计（MOS）和方差不足方法对原始整体进行了校准。校准后的系综记录了观测到的PM _2.5的日变化和每日变化非常好，平均偏差几乎为零。与所有月份的原始合奏相比，西部CONUS校准后的合奏的平均偏差降低了90-100％，而在CONUS其他地区则降低了40-100％。均方根误差（RMSE）相应降低了13-40％。与原始合奏相比，校准后的合奏还显示出RMSE和扩展匹配的改善30％。我们还表明，当在操作环境中没有足够的计算资源时，可以将基于三种气象和三种人为排放情景的组合的九人系综用作整个系综的较小子集。