Multi-scale three-dimensional variational data assimilation for high-resolution aerosol observations: Methodology and application

With an increasing number of air quality monitoring stations installed around the Chinese mainland,high-resolution aerosol observations become available,allowing improvements in air pollution monitoring and aerosol forecasting.However,the multi scales(especially small-scale)information included in high-resolution aerosol observations could not be effectively utilized by the traditional three-dimensional variational method(3DVAR).This study attempted to extend the traditional 3DVAR to a multi-scale 3DVAR with two iteration steps,two-scale-3DVAR(TS-3DVAR),to improve the effectiveness of assimilating high-resolution observations.In TS-3DVAR,the large-scale and small-scale components of observation information were decomposed from the original high-resolution observations using a Gaussian smoothing method and then assimilated using the corresponding large-scale or small-scale background error covariances which were derived from the partitioned background error samples.The data assimilation(DA)analysis field generated by TS-3DVAR is more accurate than 3DVAR in reproducing the field’s multi-scale characteristics,which could thus be used as the initial chemical field of the air quality model to improve aerosol forecasting.Particulate matter with an aerodynamic diameter of less than 2.5μm(PM_(2.5))and 10.0μm(PM_(10)) from the surface air quality monitoring stations from November 01 to November 30,2018 at 00:00 were assimilated daily to verify the effects of TS-3DVAR and 3DVAR on the aerosol analysis and forecast accuracy.The results showed that TS-3DVAR better constrained both large-scale and small-scale,especially the spatial wavelengths in a range of 54-216 km and those above 351 km.The average power spectra of the TS-3DVAR assimilation increment in the two wavelength ranges were 71.70%and 35.33%higher than those of 3DVAR.As a result,the TS-3DVAR was more effective than 3DVAR in improving the accuracy of the initial chemical field,and thereby the forecasting capability for PM_(2.5).In the initial chemical field,the 30-day average correlation coefficient(Corr)of PM_(2.5) of TS-3DVAR was 0.052(6.12%)higher than that of 3DVAR,and the root mean square error(RMSE)of TS-3DVAR was 3.446μg m^(−3)(16.4%)lower than that of 3DVAR.For the forecasting capability for PM_(2.5) mass concentration,the 30-day average Corr of TS-3DVAR during the 0-24 hour forecast period was 0.025(5.08%)higher than that of 3DVAR,and the average RMSE was 2.027μg m^(−3)(4.85%)lower.The positive effect of TS-3DVAR on the improvement of forecasting capability can last for more than 24 h.

A three-dimensional variational data assimilation system for a size-resolved aerosol model: Implementation and application for particulate matter and gaseous pollutant forecasts across China

A three-dimensional variational(3DVAR)data assimilation(DA)system is presented here based on a size-resolved sectional aerosol model,the Model for Simulating Aerosol Interactions and Chemistry(MOSAIC)within the Weather Research and Forecasting model coupled to Chemistry(WRF-Chem)model.The use of this approach means that both gaseous pollutants such as SO2,NO2,CO,and O3 as well as particulate matter(PM2.5,PM10)observational data can be assimilated simultaneously.Two one-month parallel simulation experiments were conducted,one with the assimilation of surface hourly concentration observations of the above six pollutants released by the China National Environmental Monitoring Centre(CNEMC)and one without assimilation in order to verify the impact of assimilation on initial chemical fields and subsequent forecasts.Results show that,in the first place,use of the DA system can provide a more accurate model initial field.The root-mean-square error of PM2.5,PM10,SO2,NO2,CO,and O3 mass concentrations in analysis field fell by 29.27μg m-3(53.5%),34.5μg m-3(50.9%),30.36μg m-3(64.2%),8.91μg m-3(39.5%),0.46 mg m-3(47.4%),and 15.11μg m-3(51.0%),respectively,compared to a background field without assimilation.At the same time,mean fraction error was reduced by 42.6%,53.1%,45.2%,43.1%,69.9%,and 48.8%,respectively,while the correlation coefficient increased by 0.51,0.55,0.48,0.38,0.47,0.65,respectively.Secondly,the results of this analysis reveal variable benefits from assimilation on different pollutants.DA significantly improves PM2.5,PM10,and CO forecasts leading to positive effects that last more than 48 h.The positive effects of DA on SO2 and O3 forecasts last up to 8 h but that remains relatively poor for NO2 forecasts.Thirdly,the influence of assimilation varies in different areas.It is possible that the positive effects of DA on PM2.5 and PM10 forecasts can last more than 48 h across most regions of China.Indeed,DA significantly improves SO2 forecasts within 48 h over north China,and much longer CO assimilation benefits(48 h)are found in most regions apart from north and east China and across the Sichuan Basin.DA is able to improve O3 forecasts within 48 h across China with the exception of southwest and northwest regions and the O3 DA benefits in southern China are more evident,while from a spatial distribution perspective,NO2 DA benefits remain relatively poor.