Balance of Emission and Dynamical Controls on Ozone During the Korea‐United States Air Quality Campaign From Multiconstituent Satellite Data Assimilation

Abstract

Global multiconstituent concentration and emission fields obtained from the assimilation of the satellite retrievals of ozone, CO, NO 2 , HNO 3 , and SO 2 from the Ozone Monitoring Instrument (OMI), Global Ozone Monitoring Experiment 2, Measurements of Pollution in the Troposphere, Microwave Limb Sounder, and Atmospheric Infrared Sounder (AIRS)/OMI are used to understand the processes controlling air pollution during the Korea‐United States Air Quality (KORUS‐AQ) campaign. Estimated emissions in South Korea were 0.42 Tg N for NO x and 1.1 Tg CO for CO, which were 40% and 83% higher, respectively, than the a priori bottom‐up inventories, and increased mean ozone concentration by up to 7.5 ± 1.6 ppbv. The observed boundary layer ozone exceeded 90 ppbv over Seoul under stagnant phases, whereas it was approximately 60 ppbv during dynamical conditions given equivalent emissions. Chemical reanalysis showed that mean ozone concentration was persistently higher over Seoul (75.10 ± 7.6 ppbv) than the broader KORUS‐AQ domain (70.5 ± 9.2 ppbv) at 700 hPa. Large bias reductions (>75%) in the free tropospheric OH show that multiple‐species assimilation is critical for balanced tropospheric chemistry analysis and emissions. The assimilation performance was dependent on the particular phase. While the evaluation of data assimilation fields shows an improved agreement with aircraft measurements in ozone (to less than 5 ppbv biases), CO, NO 2 , SO 2 , PAN, and OH profiles, lower tropospheric ozone analysis error was largest at stagnant conditions, whereas the model errors were mostly removed by data assimilation under dynamic weather conditions. Assimilation of new AIRS/OMI ozone profiles allowed for additional error reductions, especially under dynamic weather conditions. Our results show the important balance of dynamics and emissions both on pollution and the chemical assimilation system performance.

Source Contributions to Carbon Monoxide Concentrations During KORUS‐AQ Based on CAM‐chem Model Applications

“…The 1-degree simulations using HTAP +FINN emissions show the lowest biases (34-35%) and RMSE (108-110 ppbv) among the eight simulations (please see Table S1). On average, all these simulations lead to consistent underestimates of CO by 69-79 ppbv (34-39%), attributed most likely due to persistent underestimation of CO emissions (Bey et al, 2001;Miyazaki et al, 2018;Stein et al, 2014;Streets et al, 2006) and will be elaborated further in the following sections.…”

An intercomparison of tropospheric ozone reanalysis products from CAMS, CAMS interim, TCR-1, and TCR-2

“…go.jp/~miyazaki/tcr/ (last access: 1 July 2019; Miyazaki et al, 2015); the TCR-2 reanalysis is available from https://tes.jpl.nasa. gov/chemical-reanalysis/(last access: 1 December 2019; Miyazaki et al, 2019bMiyazaki et al, , 2020b.…”

From photon paths to pollution plumes: better radiative transfer calculations to monitor NOx emissions with OMI and TROPOMI

“…In this study, they assimilated not only satellite measurements of NO 2 but also O 3 , CO and HNO 3 from different satellites to better constraint tropospheric chemistry. This multiconstituent data assimilation approach shows a better agreement of simulated species with independent observations, as well as a reduction in simulation errors (Miyazaki et al, 2018).…”

“…The comparison results were extensively discussed and have been documented in detail in deliverables and peer-reviewed publications that are publicly available. Also the fitness for purpose of the QA4ECV dataset has been evaluated in peer-reviewed publications (Miyazaki et al, 2018) as well as in several studies from PhD and MSc students (Boersma et al, 2016a). Most of the developments in the QA4ECV retrieval algorithm have been implemented in the retrieval algorithm for the TROPOMI instrument.…”

“…satellite retrievals, but also to companies and policy makers that want to use air quality data for different purposes. Several scientific studies have already used QA4ECV data, like in the multi-constituent data assimilation study by Miyazaki et al (2018).…”