摘要:针对北京市2016年12月16~21日的空气重污染过程进行了回报试验，探讨了该次事件预报的目标观测敏感区。使用新一代高分辨率中尺度气象模式（Weather Research Forecasting，WRF）和嵌套网格空气质量模式（Nested Air Quality Prediction Model System，NAQPMS），针对初始气象场的不确定性，通过4套初始场资料识别了影响北京地区细颗粒物（PM2.5）预报水平的目标观测敏感变量及其敏感区。结果表明：当综合考虑初始气象场的风场、温度、比湿不确定性的影响时，发现改善黑龙江区域上述气象要素的初始场精度，对北京地区PM2.5预报不确定的减小最显著；当分别考察风场、温度、比湿的不确定性的影响时，发现初始风场精度的改善，尤其是黑龙江区域风场精度的改善，能够更大程度地减小北京地区PM2.5的预报误差，对北京东南地区的PM2.5预报误差的减小甚至可达到40%以上。因此，优先对黑龙江区域的气象场，尤其是该区域的风场进行目标观测，并将其同化到预报模式的初始场中，将会有效提高初始气象场的质量，进而大大减小北京地区PM2.5浓度的预报误差，提高北京地区空气质量的预报技巧。初始风场代表了北京地区该次空气重污染事件预报的目标观测变量，而黑龙江地区则是该目标观测的敏感区域。
关键词:空气污染/
数值预报/
目标观测/
北京
Abstract:A hindcast experiment is conducted for the heavy air pollution event in Beijing that occurred during 16-21 December 2016 and the sensitive area for target observation that can help to improve initialization are explored using the Weather Research Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). To address the uncertainty of meteorological initial field, the sensitive variables and areas for the prediction of PM2.5 concentration in Beijing are identified by adopting four sets of initial analysis fields. The results show that when considering the initial uncertainties of wind, temperature, and specific humidity, their reductions in Heilongjiang region can most significantly decrease the forecast error of the PM2.5 concentration in Beijing. Furthermore, it is found that the improvement of the accuracy of initial wind fields, especially that in Heilongjiang region, decreases the forecast error of the PM2.5 concentration in Beijing to a great extent, and the decrease can be up to more than 40% in southwestern Beijing. Therefore, increasing more meteorological (especially wind) observations in Heilongjiang region and assimilating these observations into the initial field of the WRF model will significantly improve the quality of the initial meteorological condition and thus greatly reduce the PM2.5 forecast error of the air pollution event in Beijing. The model forecast skill will be greatly improved. It is concluded that the wind component in the initial field represents the physical variable and Heilongjiang region is the sensitive area for target observation associated with the forecast of the heavy air pollution event in Beijing that is selected for the present study.
Key words:Air pollution/
Numerical forecast/
Target observation/
Beijing
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