摘要:针对北京市2016年12月16～21日的重污染过程，基于嵌套网格空气质量模式预报系统（NAQPMS），面向气象驱动模式WRF中7类物理过程的参数化方案，通过单扰动和组合扰动方式构建了51组不同的WRF模式运行配置，对比分析不同方案配置下NAQPMS对这次重污染过程细颗粒物（PM2.5）浓度预报的性能。结果表明：在重污染时段，组合扰动优化方案在城中心站点和城郊站点的PM2.5浓度预报精度都显著高于基准参数化方案配置下的预报结果，特别是能显著改进基准方案下模式对重污染过程结束时间的预报误差问题，显著减小12月21日存在的预报偏差。从统计指标来看，城中心站点在组合扰动优化方案下预报相关性最高，相关系数在0.7以上；从预报均方根误差来看，组合扰动优化方案误差最小。城郊站点同样是在组合扰动优化方案下预报相关性最高，与观测之间的偏差更小。从污染物与气象要素的空间分布来看，组合扰动优化方案比基准方案能更好再现污染时段的气象要素变化，预报的风速更小、相对湿度更高，从而有利于12月21日北京高浓度PM2.5的维持和累积。本文结果表明气象预报模式参数化方案不确定性是重污染预报的关键不确定性来源，选择合适的参数化方案可以减小重污染期间气象要素的模拟偏差，并可进一步提高重污染时段的PM2.5浓度预报精度。
关键词:细颗粒物（PM2.5）浓度预报/
气象参数化方案优选/
大气重污染过程/
北京
Abstract:Based on the Nested Air Quality Prediction Model System (NAQPMS), this paper is oriented toward the parameterization of seven types of physical processes in the weather-driven model, Weather Research and Forecast Model (WRF). Fifty-one sets of different WRF model operating configurations are constructed through single disturbance and combined disturbance methods. The paper compares and analyzes the performance of NAQPMS for PM2.5 concentration forecasting during the heavy pollution period in Beijing from 16–21 December 2016, under different scheme configurations. The results show that during the heavy pollution period, the PM2.5 concentration forecast accuracy of the combined disturbance optimization scheme at the central station and the suburban station is significantly higher than the forecast results under the configuration of the baseline parameterization scheme. The combined disturbance optimization scheme can significantly improve the model’s forecast error for the end time of the heavy pollution process under the baseline scheme, and significantly reduce the forecast deviation that exists on 21 December 2016. Judging from the statistical indicators, the city center station has the highest forecast correlation under the combined optimization scheme, with a correlation coefficient>0.7; from the perspective of the root mean square error of the forecast, the combined optimization scheme has the smallest error. Furthermore, suburban stations have the highest forecast correlation under the combined optimization scheme, and the deviation from the observations is smaller than that of the central station. From the perspective of the spatial distribution of pollutants and meteorological elements, the combined disturbance optimization scheme can better reproduce the changes in meteorological elements during the pollution period than the baseline scheme. The forecasted wind speed is low and the relative humidity is high, which is conducive to the maintenance and accumulation of high concentrations of PM2.5 in Beijing on 21 December. The results of this paper show that the uncertainty of the parameterization scheme of the meteorological forecasting model is the key source of uncertainty for heavy pollution forecasting. Choosing a suitable parameterization scheme can reduce the simulation deviation of meteorological elements during the heavy pollution period and further increase the PM2.5 concentration forecast accuracy during the heavy pollution period.
Key words:PM2.5 concentration forecast/
Meteorological parameterization scheme optimization/
Air Pollution Process/
Beijing
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