摘要:气象预报是影响大气重污染预报精度的关键所在。针对2016年12月16～21日北京市一次重污染过程，开展了中尺度气象模式WRF的参数化方案配置敏感性试验。对微物理过程、长波辐射过程、短波辐射过程、陆面过程、边界层过程、近地面过程以及积云对流参数化过程进行组合优选，共设计51组参数化方案组合，分析不同模拟方案下北京市8个气象站点温度、相对湿度、10 m风速的模拟精度及其敏感性。试验结果表明：温度模拟对长波过程参数化方案最为敏感，集合离散度达2.4～7.4°C，再次是短波过程参数化方案；相对湿度模拟也对长波过程参数化方案最敏感，再次是陆面过程；风速模拟对不同过程参数化方案的敏感性程度差异不大。通过模拟结果与观测的统计对比，优选出模拟误差最小的方案组合为Lin微物理方案、RRTMG长波方案、RRTMG短波方案、Tiedtke积云对流方案、Noah陆面方案、MYNN 3rd边界层方案和MYNN近地面方案，并将其与集合平均、基准方案进行对比。对于集合平均来说，其温度模拟与观测相关系数为0.69，高于基准方案，其模拟偏差与均方根误差比基准方案低25%和11%；集合平均的相对湿度和风速模拟相比基准方案变化较小。与集合平均相比，优选方案能同时改进温度、相对湿度和风速模拟，使温度模拟偏差和均方根误差比基准方案下降35%和17%，使相对湿度模拟偏差和均方根误差下降43%和13%，使风速模拟偏差和均方根误差下降33%和24%。以上结果表明，参数化方案的敏感性试验和优选能显著减小重污染期间气象要素的模拟误差，重污染预报改进需重点关注参数化方案模拟上的不确定性。本研究也发现MYNN3rd边界层方案在这次重污染过程的气象要素模拟上具有良好性能，可为未来重污染预报改进提供参考。
关键词:大气重污染/
气象模拟/
参数化方案/
敏感性试验/
WRF模式
Abstract:Meteorological forecasting is an important factor affecting the accuracy of atmospheric heavy pollution prediction. In response to a heavy pollution event in Beijing during 16?21 December 2016, this paper carried out a sensitivity test for the parameterization scheme of a mesoscale meteorological model Weather Research and Forecasting (WRF). Combining microphysical, long-wave radiation, short-wave radiation, land surface, boundary layer, near-surface, and cumulus convective parameterization processes, a total of 51 sets of parameterization schemes were designed to analyze the simulation accuracy and sensitivity of the temperature, relative humidity, and 10-m height wind speed of eight meteorological stations in Beijing under different simulation schemes. The temperature simulation is the most sensitive to a long-wave process parameterization scheme, the set dispersion is 2.4–7.4°C, followed by the short-wave process parameterization scheme. Additionally, the relative humidity simulation is the most sensitive to the long-wave process parameterization scheme, followed by the land surface process and the wind speed simulation had little difference in sensitivity to different process parameterization schemes. In the comparison of the statistical results of the simulation results with observations, we prefer the combination of the smallest simulation error: Lin microphysical, RRTMG long-wave, RRTMG short-wave, Tiedtke cumulus convection, Noah land surface, MYNN 3rd boundary layer and MYNN near-surface scheme, and compared the best scheme to the ensemble mean and baseline scheme. For the ensemble mean, the correlation coefficient between the temperature simulation and observation was 0.69, which is greater than the baseline scheme. The simulated deviation and root-mean-square error were 25% and 11% less than the baseline scheme and the ensemble mean relative humidity and wind speed simulation were less variable than the baseline scheme. Compared with the ensemble mean, the best scheme can simultaneously improve the temperature, relative humidity, and wind speed simulation, such that the temperature simulation deviation and root-mean-square error decreases by 35% and 17% compared with the baseline scheme, the relative humidity simulation deviation and root-mean-square error decreases by 43% and 13%, and the wind speed simulation deviation and root-mean-square error decreases by 33% and 24%. The above results show that the sensitivity test and optimization of the parameterization scheme can significantly reduce the simulation error of meteorological elements during heavy pollution. The improvement of heavy pollution prediction needs to focus on the uncertainty of the parametric scheme simulation. Additionally, the MYNN 3rd boundary layer scheme has good performance in the simulation of meteorological elements in this heavy pollution process, which can provide reference for future improvements of heavy pollution forecasting.
Key words:Atmospheric heavy pollution/
Meteorological simulation/
Parameterization scheme/
Sensitivity test/
WRF model
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