IMPROVEMENTS TO THE RETRIEVAL OF NO₂ COLUMN FOR THE GOME-2 INSTRUMENT

Model evaluations against ground observations are usually unfair. The former simulates mean status over coarse grids and the latter the surrounding atmosphere. To solve this, we proposed the new land-use-based representative (LUBR) operator that considers intra-grid variance. The LUBR operator is validated to provide insights that align with satellite measurements. The results highlight the importance of considering fine-scale urban–rural differences when comparing models and observation.

We quantify the benefit of multisource observations (GEMS, LEO satellite, and surface) on ozone simulations in Asia. Data assimilation improves the monitoring of exceedance, spatial pattern, and diurnal variation of surface ozone, with the regional mean bias reduced from −2.1 to −0.2 ppbv. Data assimilation also better represents ozone vertical distributions in the middle to upper troposphere at low latitudes. Our results offer a valuable reference for future ozone simulations.

Lightning, one of the major sources of nitrogen oxides in the atmosphere, contributes to the tropospheric concentration of ozone and to the oxidizing capacity of the atmosphere. In this work, we contribute to improving the estimation of lightning-produced nitrogen oxides in the Ebro Valley and the Pyrenees by using two different TROPOMI products and comparing the results.

In this work, an improved tropospheric NO₂ retrieval algorithm from TROPOMI measurements over Europe is presented. The stratospheric estimation is implemented with correction for the dependency of the stratospheric NO₂ on the viewing geometry. The AMF calculation is implemented using improved surface albedo, a priori NO₂ profiles, and cloud correction. The improved tropospheric NO2 data show good correlations with ground-based MAX-DOAS measurements.

This paper presents an improved tropospheric nitrogen dioxide (NO₂) retrieval algorithm from the Global Ozone Monitoring Experiment-2 (GOME-2) instrument based on air mass factor (AMF) calculations that are performed with a more accurate knowledge of surface albedo, the a priori NO₂ profile, and cloud and aerosol corrections.