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首页> 外文期刊>Atmospheric environment >Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States
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Application of WRF/Chem-MADRID for real-time air quality forecasting over the Southeastern United States

机译：WRF / Chem-MADRID在美国东南部实时空气质量预测中的应用

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A Real-lime Air Quality horecast (Kl-AQh) system that is based on a tnree-dimensional air quality model provides a powerful tool to forecast air quality and advise the public with proper preventive actions. In this work, a new RT-AQF system is developed based on the online-coupled Weather Research and Forecasting model with Chemistry (WRF/Chem) with the Model of Aerosol Dynamics, Reaction, loniza-tion, and Dissolution (MADRID) (referred to as WRF/Chem-MADRID) and deployed in the southeastern U.S. during May-September, 2009. Max 1-h and 8-h average ozone (O3) and 24-h average fine paniculate matter (PM2.5) are evaluated against surface observations from the AIRNow database in terms of spatial distribution, temporal variation, and domain-wide and region-specific discrete and categorical performance statistics. WRF/Chem-MADRID demonstrates good forecasting skill that is consistent with current RT-AQF models. The overpredictions of O3 and underprediction of PM2.5 are likely due to uncertainties in emissions such as those of biogenic volatile organic compounds (BVOCs) and ammonia, inaccuracies in simulated meteorological variables such as 2-m temperature, 10-m wind speed, and precipitation, and uncertainties in the boundary conditions. Sensitivity simulations show that the use of the online BVOC emissions can improve PM2.5 forecast in areas with high BVOC emissions and adjusting lateral boundaries can improve domain-wide O3 and PM2.5 predictions. Several limitations and uncertainties are identified to further improve the model's forecasting skill.
机译：基于二维空气质量模型的实时石灰空气质量播报（Kl-AQh）系统提供了强大的工具来预测空气质量并采取适当的预防措施向公众提供建议。在这项工作中，基于在线耦合的天气研究与化学预测模型（WRF / Chem）和气溶胶动力学，反应，离子化和溶解模型（MADRID），开发了一个新的RT-AQF系统（称为在2009年5月至9月期间部署在美国东南部。对最大1小时和8小时平均臭氧（O3）和24小时平均细颗粒物（PM2.5）进行了评估从AIRNow数据库进行的表面观测，包括空间分布，时间变化以及全域和特定于区域的离散和分类性能统计信息。 WRF / Chem-MADRID具有良好的预测能力，与当前的RT-AQF模型一致。 O3的高估和PM2.5的低估可能归因于排放的不确定性，例如生物挥发性有机化合物（BVOC）和氨的排放不确定性，模拟气象变量（如2米温度，10米风速和降水和边界条件的不确定性。敏感性模拟显示，使用在线BVOC排放量可以改善BVOC排放量高的地区的PM2.5预测，调整横向边界可以改善全域O3和PM2.5的预测。确定了一些局限性和不确定性，以进一步提高模型的预测能力。

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