Blended LEO-GEO Tropospheric Column NO2 for Air Quality Applications

Abstract

With the launch of Tropospheric Emissions: Monitoring of Pollution (TEMPO) and Geostationary Environment Monitoring System (GEMS) instruments in geostationary (GEO) orbit, we are entering a new era of air quality monitoring.  These hourly observations of trace gases and aerosols need to be thoroughly evaluated for the time of the day biases that stem from solar-satellite viewing conditions.  Because observations made from low earth orbit (LEO) satellite sensors such as TROPOspheric Monitoring Instrument (TROPOMI) and Ozone Mapping and Profiler Suite (OMPS) are well characterized, comparisons can be made to understand viewing geometry dependent biases in GEMS and TEMPO retrievals and develop soft calibration methods.  We developed a Kalman filter approach that combines tropospheric nitrogen dioxide column (tropNO2) data from TROPOMI and GEMS. The spatial and temporal variability in tropNO2 comes from GEMS whereas TROPOMI tropNO2 observations are added to the Kalman filter process as observations that nudge GEMS tropNO2.  The relative weighting is influenced by the observation error covariance from GEMS and TROPOMI. Analysis of the GEMS tropNO2 data reveals its systematic biases, while applying the Kalman filter method to merge the GEMS and TROPOMI will yield a more accurate NO2 product. Accuracy of the merged tropNO2 data is determined by comparing the merged dataset against ground-based measurements from the Pandonia Global Network. We will present the methodology and case studies application of the merged dataset.