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.