Impacts of geostationary satellite measurements on CO forecasting: An observing system simulation experiment with GEOS-Chem/LETKF data assimilation system

We developed a chemical data assimilation system based on the GEOS-Chem global chemical transport model (CTM) and an ensemble-based data assimilation method, and performed an observing system simulation experiment (OSSE) to evaluate the impact of geostationary (GEO) satellite data obtained with a multi-spectral (thermal infrared (TIR) and near infrared (NIR)) sensor on air quality forecasting in East Asia. l conditions determined by assimilation of the three observation sets improved the forecasting of trans-boundary CO outflow. The performance of GEO satellite with TIR sensor (GEO-TIR) was better than that of LEO satellite with TIR sensor (LEO-TIR). However, in Seoul district (the Korean Peninsula) and Northern Kyushu (western Japan), the positive impact of the wider coverage and higher frequency of GEO disappeared when the forecast time was longer than 48 h. GEO satellite with NIR and TIR sensor (GEO-NIR + TIR) improved the forecast most, reducing the root mean square difference (RMSD), normalized mean bias, and normalized mean difference by more than 20% even for a forecast time longer than 48 h. the LEO-TIR result as a benchmark, we evaluated the ability of GEO-NIR + TIR to improve the forecast. The 60-h CO forecasting performances of GEO-TIR and GEO-NIR + TIR were about 30% and 120% better, respectively, than that of LEO-TIR. The wider coverage and higher frequency of GEO therefore improved the RMSD by 30%, and the higher sensitivity in the lower troposphere of NIR + TIR improved it by an additional 90%. Thus, the higher sensitivity in the lower troposphere of NIR + TIR as well as the wider coverage and higher frequency of GEO had a notably positive impact on the forecasting of trans-boundary pollutants over East Asia.