基于Gauss烟团模型的大气扩散数据同化方法
Data assimilation method for atmospheric dispersion based on a Gaussian puff model

发生核事故后，可以通过模型迅速预测泄漏的放射性物质的大气扩散情况，但释放源项、气象等参数的不确定性导致大气扩散模型计算的准确性受到影响。通常可以采用数据同化来改善模型预测结果。该文提出一种基于Gauss烟团模型的大气扩散数据同化方法，可以结合观测数据改善模型的预测结果。该方法在迭代搜索中对烟团参数进行线性变换以简化Gauss烟团模型，利用粒子群优化算法对泄漏速率、释放高度、风向、平均风速这4个模型参数进行校正。该方法适用于平坦地形和均匀稳定气象条件下的中尺度扩散。采用稳态条件下双生子实验进行验证，观测点位的模拟值与真实观测值的相关系数达到0.99。在非稳态条件下对源项估计结果进行了测试，结果略优于集合Kalman滤波方法，相关系数达到0.68。该同化方法计算速度快，能有效提升模型的预测，可用于大气扩散数据同化。
Abstract：Models are needed to quickly predict the atmospheric dispersion of radioactive material released in a nuclear accident. However, the uncertainties in the source term, meteorological data, and other conditions reduce the dispersion model prediction reliability. Data assimilation (DA) is usually introduced to improve the model predictions. The paper presents a DA method based on a Gaussian puff model to improve the predictions using some observed data. The method modifies the puff parameters to approximate the observed data in an iterative search. The four model parameters modified using particle swarm optimization in this study are the release rate, release height, wind direction, and mean wind speed. The method is applicable to mesoscale atmospheric dispersion with uniform and stable conditions over a flat area. Twin experiments are used to verify this DA method. The correlation coefficient between the experimental group and the control group at the observation points is 0.99. The source estimation in the non-steady condition is also tested with the correlation coefficient of 0.68, slightly better than the ensemble Kalman filter method. The method converges rapidly with good model predictions; thus, this method is useful for data assimilation of atmospheric dispersion.