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Predicting air quality: Improvements through advanced methods to integrate models and measurements

机译:预测空气质量:通过先进的方法进行改进,以整合模型和测量结果

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Air quality prediction plays an important role in the management of our environment. Computational power and efficiencies have advanced to the point where chemical transport models can predict pollution in an urban air shed with spatial resolution less than a kilometer, and cover the globe with a horizontal resolution of less than 50 km. Predicting air quality remains a challenge due to the complexity of the governing processes and the strong coupling across scales. While air quality prediction is closely aligned with weather prediction, there are important differences, including the role of pollution emissions and their associated large uncertainties. Improvements in air quality prediction require a close integration of observations. As more atmospheric chemical observations become available chemical data assimilation is expected to play an essential role in air quality forecasting. In this paper advances in air quality forecasting are discussed with an emphasis on data assimilation. Applications of the four-dimensional variational method (4D-Var) and the ensemble Kalman filter (EnKF) approach are presented and the computation challenges are discussed. (c) 2007 Elsevier Inc. All rights reserved.
机译:空气质量预测在环境管理中起着重要作用。计算能力和效率已经发展到这样的程度:化学迁移模型可以预测空间分辨率小于一公里的城市棚屋中的污染,水平分辨率小于50 km的覆盖地球。由于控制过程的复杂性和跨刻度的强耦合,预测空气质量仍然是一个挑战。尽管空气质量预测与天气预报紧密相关,但仍存在重要差异,包括污染排放的作用及其相关的较大不确定性。空气质量预测的改进需要对观测结果进行紧密整合。随着越来越多的大气化学观测变得可用,化学数据的同化将在空气质量预测中发挥至关重要的作用。本文讨论了空气质量预测方面的进展,重点是数据同化。提出了二维变分方法(4D-Var)和集成卡尔曼滤波(EnKF)方法的应用,并讨论了计算难题。 (c)2007 Elsevier Inc.保留所有权利。

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