Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions
1Royal Netherlands Meteorological Institute (KNMI), Wilhelminalaan 10, 3732 GK, De Bilt, The Netherlands
2Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
3Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
4Eindhoven University of Technology, Fluid Dynamics Lab, The Netherlands
Abstract. We have developed an advanced chemical data assimilation system to combine observations of chemical compounds from multiple satellites. NO2, O3, CO, and HNO3 measurements from the OMI, TES, MOPITT, and MLS satellite instruments are assimilated into the global chemical transport model CHASER for the years 2006–2007. The CHASER data assimilation system (CHASER-DAS), based on the local ensemble transform Kalman filter technique, simultaneously optimizes the chemical species, as well as the emissions of O3 precursors, while taking their chemical feedbacks into account. With the available datasets, an improved description of the chemical feedbacks can be obtained, especially related to the NOx-CO-OH-O3 set of chemical reactions. Comparisons against independent satellite, aircraft, and ozonesonde data show that the data assimilation results in substantial improvements for various chemical compounds. These improvements include a reduced negative tropospheric NO2 column bias (by 40–85%), a reduced negative CO bias in the Northern Hemisphere (by 40–90%), and a reduced positive O3 bias in the middle and upper troposphere (from 30–40% to within 10%). These changes are related to increased tropospheric OH concentrations by 5–15% in the tropics and the Southern Hemisphere in July. Observing System Experiments (OSEs) have been conducted to quantify the relative importance of each data set on constraining the emissions and concentrations. The OSEs confirm that the assimilation of individual data sets results in a strong influence on both assimilated and non-assimilated species through the inter-species error correlation and the chemical coupling described by the model.