Assimilation of lidar and ceilometer observations from the E-profile network of European ground-based stations into ECMWF’s Integrated Forecasting System (IFS-COMPO)

The Integrated Forecasting System, extended for atmospheric composition modelling (IFS-COMPO), is a global forecasting system for atmospheric trace gases and aerosols. It provides the global component for the Copernicus Atmosphere Monitoring Service (CAMS). In the operational suite of the IFS-COMPO aerosol concentrations are constrained by assimilating aerosol optical depth (AOD) from different satellites. Here, we test the system by adding assimilating of ground-based lidar and ceilometer observations from the European E-Profile network. The performance is investigated by comparison to non-assimilated E-Profile stations, AERONET AOD observations, and aerosol ground concentrations from AirBase. E-Profile assimilation strongly reduces biases and root mean square errors (RMSE) of model-equivalent profiles of the attenuated backscatter coefficient. When constraining aerosols with AOD observations only, surface concentrations of particles smaller than 2.5 μm (PM2.5) are often overestimated in summer, and concentrations of particles smaller than 10 μm (PM10) are frequently underestimated. Additional assimilation of E-Profile observations can lower the RMSE of PM2.5 by up to 50% and of PM10 by up to 10 %. However, as the IFS-COMPO analysis system uses the total aerosol mass mixing ratio as control variable, the positive PM2.5 bias and the negative PM10 bias cannot simultaneously be improved. In most cases the PM2.5 bias is reduced, while the PM10 bias is degraded. The reason is that fine particles make the dominant contribution to the optical cross sections per mass. Different configurations of the assimilation-system have been tested, showing that the best overall performance is achieved by describing optical properties of dust with a spheroid model, suppressing vertical correlations in the background error covariances, and using an aggressive cloud mask.