摘要
【目的】在使用AERMOD模式进行复杂地形区域大气预测时,控制气象条件与污染源源强等参数相同,采用分辨率更高的DEM地形数据可使得预测结果更加精确。【方法】设置气象条件相同,对于同一无组织面源,在复杂山地、复杂河谷2种地形条件下分别进行不添加地形数据、添加90 m×90 m精度地形数据、添加30 m×30 m精度地形数据的大气预测。【结果】在复杂山地地形条件下,不添加地形数据、添加90 m×90 m精度地形数据下预测结果与添加30 m×30 m精度地形数据下最优预测结果相关性分别为24.0%与52.4%;在复杂河谷地形条件下,不添加地形数据、添加90 m×90 m精度地形数据下预测结果与添加30 m×30m精度地形数据下最优预测结果相关性分别为48.7%与89.3%。【结论】在使用AERMOD模式进行大气预测时,若面源为复杂山地地形,建议采用30 m×30 m精度地形数据,以提升预测结果的准确性。
[ Objective ] Using the AERMOD model for atmospheric prediction of complex terrain areas by controlling meteorological conditions and pollution sources with other parameters of the same, the application of higher resolution DEM terrain data could make prediction results more accurate. [ Method] For the same unorganized surface source, firstly the same meteorological conditions were set, and then under the two terrain conditions, the three atmospheric predictions （adding no terrain data, adding 90 m X 90 m precision terrain data, adding 30 m X 30 m precision terrain data） were carried out. [ Result] Under complicated mountain terrain condition, the correlations of the prediction re- suits under adding no terrain data and adding the 90 m X 90 m precision terrain data with the optimal prediction results under adding the 30 m X 30 m precision terrain data was 24.0 % and 52.4 %. In complicated valley terrain, the correlation of the prediction results under adding no terrain data and adding the 90 m X 90 m precision terrain data between the optimal prediction results under adding the 30 m X 30 m precision terrain data were 48.7 % and 89.3 %. [ Conclusion] The present study indicated that when using the AERMOD for atmospheric prediction, if the surface source was located in a complicated mountain terrain, it was reconunended to use the 30 m X 30 m precision terrain data to improve the accuracy of the forecast results.
作者
关勖
仝纪龙
莫欣岳
潘峰
谢南洪
GUAN Xu;TONG Ji-long;MO Xin-yue;PAN Feng;XIE Nan-hong(College of Atmospheric Science,Lanzhou University,Gansu Lanzhou 730000,China)
出处
《西南农业学报》
CSCD
北大核心
2018年第6期1288-1292,共5页
Southwest China Journal of Agricultural Sciences
基金
国家杰出青年科学基金项目(41225018)
