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Source Reconstruction of Atmospheric Releases with Limited Meteorological Observations Using Genetic Algorithms

机译：利用遗传算法利用有限的气象观测资料重建大气排放物

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摘要

A genetic algorithm is paired with a Lagrangian puff atmospheric model to reconstruct the source characteristics of an atmospheric release. Observed meteorological and ground concentration measurements from the real-world Dipole Pride controlled release experiment are used to test the methodology. A sensitivity study is performed to quantify the relative contribution of the number and location of sensor measurements by progressively removing them. Additionally, the importance of the meteorological measurements is tested by progressively removing surface observations and vertical profiles. It is shown that the source term reconstruction can occur also with limited meteorological observations. The proposed general methodology can be applied to reconstruct the characteristics of an unknown atmospheric release given limited ground and meteorological observations.
机译：遗传算法与拉格朗日抽吸大气模型配对以重构大气释放的源特征。从现实世界中的Dipole Pride控释实验中观察到的气象和地面浓度测量值用于测试该方法。通过逐步删除敏感度，可以量化传感器测量的数量和位置的相对贡献。另外，通过逐步删除地面观测资料和垂直剖面图来测试气象测量的重要性。结果表明，在气象观测有限的情况下，源项重构也可能发生。在有限的地面和气象观测条件下，提出的通用方法可用于重建未知大气释放的特征。

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