A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mas...A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mass and number size distributions of aerosols. In this paper, a parameterization scheme for aerosol size distribution in initial emission,which took into account the measured mass and number size distributions of aerosols, was developed in the GRAPES–CUACE [Global/Regional Assimilation and Pr Ediction System–China Meteorological Administration（CMA） Unified Atmospheric Chemistry Environment model]—an online chemical weather forecast system that contains microphysical processes and emission, transport, and chemical conversion of sectional multi-component aerosols. In addition, the competitive mechanism between nucleation and condensation for secondary aerosol formation was improved, and the dry deposition was also modified to be in consistent with the real depositing length. Based on the above improvements, the GRAPES–CUACE simulations were verified against observational data during 1–31 January 2013, when a series of heavy regional haze–fog events occurred in eastern China. The results show that the aerosol number size distribution from the improved experiment was much closer to the observation, whereas in the old experiment the number concentration was higher in the nucleation mode and lower in the accumulation mode. Meanwhile, the errors in aerosol number size distribution as diagnosed by its sectional mass size distribution were also reduced. Moreover, simulations of organic carbon, sulfate, and other aerosol components were improved and the overestimation as well as underestimation of PM2.5 concentration in eastern China was significantly reduced,leading to increased correlation coefficient between simulated and observed PM2.5 by more than 70%. In the remote areas where bad simulation results were produced previously, the correlation coefficient grew from 0.35 to 0.61, and the mean mass concentration went up from 43% to 87.5% of the observed value. Thus, the simulation of particulate matters in these areas has been improved considerably.展开更多
We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE（Global/Regional Assimilation and Prediction System coupled with the China Meteoro...We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE（Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System）. The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 December 2016 as the objective time when PM2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m^–3 just 1 h before the objective time, suggesting that PM2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period.The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%,and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m^-3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m^–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m^–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.展开更多
基金Supported by the National Key Project of the Ministry of Science and Technology of China(2016YFC0203306)National Natural Science Foundation of China(91544232)+1 种基金National Science and Technology Support Program of China(2014BAC16B03)China Meteorological Administration Innovation Team Fund for Haze–Fog Monitoring and Forecasts
文摘A coupled aerosol–cloud model is essential for investigating the formation of haze and fog and the interaction of aerosols with clouds and precipitation. One of the key tasks of such a model is to produce correct mass and number size distributions of aerosols. In this paper, a parameterization scheme for aerosol size distribution in initial emission,which took into account the measured mass and number size distributions of aerosols, was developed in the GRAPES–CUACE [Global/Regional Assimilation and Pr Ediction System–China Meteorological Administration（CMA） Unified Atmospheric Chemistry Environment model]—an online chemical weather forecast system that contains microphysical processes and emission, transport, and chemical conversion of sectional multi-component aerosols. In addition, the competitive mechanism between nucleation and condensation for secondary aerosol formation was improved, and the dry deposition was also modified to be in consistent with the real depositing length. Based on the above improvements, the GRAPES–CUACE simulations were verified against observational data during 1–31 January 2013, when a series of heavy regional haze–fog events occurred in eastern China. The results show that the aerosol number size distribution from the improved experiment was much closer to the observation, whereas in the old experiment the number concentration was higher in the nucleation mode and lower in the accumulation mode. Meanwhile, the errors in aerosol number size distribution as diagnosed by its sectional mass size distribution were also reduced. Moreover, simulations of organic carbon, sulfate, and other aerosol components were improved and the overestimation as well as underestimation of PM2.5 concentration in eastern China was significantly reduced,leading to increased correlation coefficient between simulated and observed PM2.5 by more than 70%. In the remote areas where bad simulation results were produced previously, the correlation coefficient grew from 0.35 to 0.61, and the mean mass concentration went up from 43% to 87.5% of the observed value. Thus, the simulation of particulate matters in these areas has been improved considerably.
基金Supported by the National Natural Science Foundation of China(41575151 and 91644223)
文摘We traced the adjoint sensitivity of a severe pollution event in December 2016 in Beijing using the adjoint model of the GRAPES–CUACE（Global/Regional Assimilation and Prediction System coupled with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Forecasting System）. The key emission sources and periods affecting this severe pollution event are analyzed. For comaprison, we define 2000 Beijing Time 3 December 2016 as the objective time when PM2.5 reached the maximum concentration in Beijing. It is found that the local hourly sensitivity coefficient amounts to a peak of 9.31 μg m^–3 just 1 h before the objective time, suggesting that PM2.5 concentration responds rapidly to local emissions. The accumulated sensitivity coefficient in Beijing is large during the 20-h period prior to the objective time, showing that local emissions are the most important in this period.The accumulated contribution rates of emissions from Beijing, Tianjin, Hebei, and Shanxi are 34.2%, 3.0%, 49.4%,and 13.4%, respectively, in the 72-h period before the objective time. The evolution of hourly sensitivity coefficient shows that the main contribution from the Tianjin source occurs 1–26 h before the objective time and its peak hourly contribution is 0.59 μg m^-3 at 4 h before the objective time. The main contributions of the Hebei and Shanxi emission sources occur 1–54 and 14–53 h, respectively, before the objective time and their hourly sensitivity coefficients both show periodic fluctuations. The Hebei source shows three sensitivity coefficient peaks of 3.45, 4.27, and 0.71 μg m^–3 at 4, 16, and 38 h before the objective time, respectively. The sensitivity coefficient of the Shanxi source peaks twice, with values of 1.41 and 0.64 μg m^–3 at 24 and 45 h before the objective time, respectively. Overall, the adjoint model is effective in tracking the crucial sources and key periods of emissions for the severe pollution event.
