Improvement Of Pm_2.5 Foreacst By Data Assmilation Of Ground And Lidar Observation

At present,China faces severe PM2 5 pollution problems.PM2 5 has an important impact on human health,visibility and climate.Numerical prediction based on the air quality model is an important technical method to deal with the current serious haze pollution.Due to factors such as emissions,meteorological field and the uncertainty of the model itself,the air quality model still has great uncertainty in PM2 5 forecast.Reducing the model uncertainty and improving the forecast effect of model is both a hot issue in current scientific research and an urgent need for the government.The simulation results of air quality model and atmospheric observation data can be coupled by data simulation(DA)to revise the model initial conditions and improve the prediction effect of the air quality model.Data assimilation of PM2.5 forecast is mainly focused on the assimilation of ground observation data and satellite remote sensing data,as the lidar observation data is difficult to obtain and the observation objects are not completely consistent with the model variables.In this paper,we evaluate the improvement of PM2 5 forecast using the assimilation of ground and lidar observation data,based on NAQPMS.Before the data assimilation experiments,regional impact of a PM2 5 pollution process was studied based on NAQPMS,and the simulation results of NAQPMS were briefly evaluated.The study show that the simulated values were in good agreement with the observed values.The model can reflect the changes and transport processes of PM2 5 during the pollution process reasonably,indicating that NAQPMS holds a good grasp of the overall trend of the spatio-temporal evolution of pollutants and provides a relatively reasonable assimilation background concentration.But there is still a certain difference between model and observation,which means that data assimilation is very necessary.A routine air quality data assimilation(DA)system was established at the China National Environmental Monitoring Center(CNEMC)based on the optimal interpolation(OI)method.The surface observations from more than 1,400 stations across China were assimilated into a real-time air quality forecast system with three nested domains.The initial conditions of NO2,SO2 and PM2 5 in the three domains were optimized by the data assimilation system.The impact of the data assimilation on the real-time PM2.5 forecast over the Beijing-Tianjin-Hebei(BTH)Region during the heavy haze season of 2015 was evaluated.The results show that the DA can significantly improve real-time PM2 5 forecasts,reducing the root mean square error(RMSE)by 23%,8.2%and 4.8%in the forecasts of the first,second and third day,respectively.The mean fractional bias and the mean fractional error of the forecast were reduced from 50.9%and 70.67%to 40%and 62.3%,respectively,and the performance changed from "criteria" to approaching "goal"(as defined by Boylan and Russell,2006).Additionally,increasing the assimilation frequency can improve the DA system performance for real-time forecasts.As can be seen from the various cases studied here,the improvement in data assimilation is more significant when the bias of the model is higher and there is still much room for correction.The results also show a rapid decay of the DA effects on the PM2 5 forecast,which highlights the limitations of the current routine data assimilation system in which only initial conditions are optimized.Further improvements in the data assimilation system with meteorological data assimilation and chemical parameter optimization are needed.Based on the assimilation of ground observation,a new lidar observation data assimilation algorithm was designed in this paper.In this new algorithm,we use the aerosol extinction coefficient observed by lidar and the PM2 5 concentration of second-order vertical field in model assimilated by ground observation using optimal interpolation to correct the PM2 5 concentration of different initial vertical layers.Based on this new assimilation algorithm,three groups of simulation experiments including unassimilated forecasting,assimilation of ground observation forecasting and assimilation of lidar observation forecasting were set up to evaluate the impact of lidar observational assimilation on PM2.5 forecast.The results show that the lidar observation can significantly correct the vertical layer of the initial field,and the vertical layer concentration distribution of the pollutant after the assimilation is more consistent with natural situation.The vertical distribution of the simulated PM2 5 concentration is more closer to aerosol extinction coefficient observed by lidar after data assimilation.But there is still a certain different between simulation and observation.The reason is that the observation point of lidar is less,the effect of assimilation are limited,and the increase of forecast time also leads to the attenuation of the assimilation effect.The improvement of 24-hour forecasting with assimilation of lidar observation is significantly in some cities.For example,before and after assimilation in Handan city,the correlation coefficient increased from 0.3 to 0.39 and 0.46,and the RMSE decreased from 94.8 before assimilation to 79.3(about 16%lower)and 60.5(It is reduced by about 36%).But not all cities have noticeably improved.The reason is that with the increase of the forecast time,the uncertainty of emission,meteorological field,and the model itself has gradually become the main cause of forecast deviation.And as the vertical layer assimilation in the initial field takes a long time impact,it is likely to enlarge these the error caused by these uncertainties.The assimilation effect of lidar observation is small in the initial stage of the simulation.After then,the assimilation effect gradually appears,and its influence time is longer than the assimilation of ground observation.Taking the influence of Handan City as an example,the impact of assimilation of lidar observation is mainly concentrated between the 10th and 30th forecasting times,and the highest can exceed 20%of the assimilation of ground observation,then the assimilation effect starts to decay slowly.There is a weak influence till the 72nd hour.