首页 > , Vol. , Issue () : -
为了验证风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据对地面PM2.5的污染过程预报的效果，本文基于WRF-Chem (Weather Research and Forecasting model coupled with Chemistry)大气化学模式和三维变分同化方法，针对2020年2月10-12日我国北方地区的一次PM2.5重污染过程，进行了同化和预报试验研究。同化数据来自常规地面站点的PM2.5浓度数据和风云三号D星MERSI传感器的气溶胶光学厚度（AOD）数据。控制试验不同化任何资料，3组同化试验分别为仅同化地面PM2.5，仅同化卫星AOD，以及同时同化PM2.5和卫星AOD两种资料。结果表明3组同化试验都可以有效提高初始场准确率，以地面PM2.5作为检验标准，仅同化PM2.5、仅同化AOD、同时同化两种资料相对于控制试验，初始场的平均偏差分别降低54.9%、21.9%和49.0%，平均相关系数分别提升51.4%、16.0%和34.0%，平均均方根误差分别降低50.6%、17.2%和42.3%。以卫星AOD作为检验标准，3组同化试验相对于控制试验，初始场的平均偏差分别降低37.6%、78.4%和83%，平均均方根误差分别降低31.6%、62.2%和65.2%。同化后的初始场对预报有显著的改进，改进持续时间达24小时，以地面PM2.5作为检验标准，同时同化两种资料的试验对24小时预报的平均偏差减少19.7%，相关系数提升8.8%，均方根误差减少17.2%；以卫星AOD作为检验标准，24小时预报的平均偏差减少40.1%，相关系数提升25.9%，均方根误差降低34.7%。试验结论为，相对于仅同化地面PM2.5资料，同化风云卫星AOD资料可以提升后期预报效果。
Objective： Verify the effect of the aerosol optical thickness data of the Fengyun-3D satellite MERSI sensor on the pollution process prediction of PM2.5. Method： This paper was based on WRF-Chem (Weather Research and Forecasting Model Coupled with) Atmospheric Chemistry model and 3-dimensional variational assimilation method, which were used to study the assimilation and prediction of a PM2.5 pollution process in northern China during February 10-13, 2020. The assimilation data were derived from PM2.5 concentration data from conventional ground stations and aerosol optical depth (AOD) data from the MERSI sensor on the Fengyun-3D satellite. The control experiment did not assimilate any data. The three groups of assimilation experiments were to assimilate ground PM2.5, satellite AOD, and PM2.5 and AOD data at the same time. Result： The results show that the three groups of assimilation experiments can effectively improve the accuracy of the initial field. Taking ground PM2.5 as the test standard, compared with the control experiment, assimilating PM2.5 data, AOD data, and PM2.5 and AOD data at the same time, the average mean deviation of the initial field was decreased by 54.9%, 21.9% and 49.0%, the average correlation coefficent was increased by 51.4%, 16.0% and 34.0%, and the average root-mean-square error was decreased by 50.6%, 17.2% and 42.3%, respectively. Taking AOD as the test standard, compared with the control experiment, the average mean deviation of the initial field in three assimilation experiments was decreased by 37.6%, 78.4% and 83%, and the average root-mean-square error was decreased by 31.6%, 62.2% and 65.2%, respectively. The initial field after assimilation can significantly improve the prediction, and the improvement lasted for more than 24 hours. In general, the experiment of assimilating two kinds of data at the same time had the best improvement effect on the 24 hour prediction. Taking ground PM2.5 as the test standard, the average mean deviation of the 24-hour forecast was decreased by 19.7%, the correlation coefficent was increased by 8.8%, and the root-mean-square error was decreased by 17.2. Taking AOD as the test standard, the average mean deviation of 24-hour forecast was decreased by 40.1%, the correlation coefficent was increased by 25.9%, the root-mean-square error was decreased by 34.7%. Conclusion： The experiment also found that, compared with only assimilating ground PM2.5 data, the assimilation of Fengyun-3D satellite AOD data had a better lasting effect on the late prediction.