Quantifying the diurnal variation in atmospheric NO2 from Geostationary Environment Monitoring Spectrometer (GEMS) observations

Abstract

The Geostationary Environment Monitoring Spectrometer (GEMS) over Asia is the first geostationary Earth orbit instrument in the virtual constellation of sensors for atmospheric chemistry and composition air quality research and applications. For the first time, the hourly observations enable studies of diurnal variation in several important trace gas and aerosol pollutants including nitrogen dioxide (${\mathrm{NO}}_{\mathrm{2}}$), which is the focus of this work. ${\mathrm{NO}}_{\mathrm{2}}$ is a regulated pollutant and an indicator of anthropogenic emissions in addition to being involved in tropospheric ozone chemistry and particulate matter formation. We present new quantitative measures of ${\mathrm{NO}}_{\mathrm{2}}$ tropospheric column diurnal variation which can be greater than 50 % of the column amount, especially in polluted environments. The ${\mathrm{NO}}_{\mathrm{2}}$ distribution is seen to change hourly and can be quite different from what would be seen by a once-a-day low-Earth-orbit satellite observation. We use GEMS data in combination with TROPOspheric Monitoring Instrument (TROPOMI) satellite and Pandora ground-based remote sensing measurements and Multi-Scale Infrastructure for Chemistry and Aerosols (Version 0, MUSICAv0) 3D chemical transport model analysis to examine the ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation in January and June 2023 over Northeast Asia and Seoul, South Korea, study regions to distinguish the different emissions, chemistry, and meteorological processes that drive the variation. Understanding the relative importance of these processes will be key to including pollutant diurnal variation in models aimed at determining true pollutant exposure levels for air quality studies. The work presented here also provides a path for investigating similar ${\mathrm{NO}}_{\mathrm{2}}$ diurnal cycles in the new Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution (TEMPO) data over North America, and later over Europe with Sentinel-4.

1 Introduction

Predicting atmospheric air quality (AQ) requires understanding the processes that emit air pollutants, how these are transported in the atmosphere, the chemical and physical transformations that take place, and the potential impact on health and the environment. Satellite observations provide valuable information on these processes; however, until recently, measurements from an individual platform were limited to twice daily at best when relying on observations from low Earth orbit (LEO). This is now changing with daylight hourly observations of atmospheric trace gases and aerosols from the new geostationary Earth orbit (GEO) satellite sensors. The South Korean GEO-KOMPSAT-2/GEMS (Geostationary Korea Multi-Purpose Satellite-2/Geostationary Environment Monitoring Spectrometer) instrument (Kim et al., 2020) has been operational over Asia since February 2020, NASA's EVI-1 TEMPO (Earth Venture Instrument-1 Tropospheric Emissions: Monitoring Pollution) instrument (Zoogman et al., 2017) was launched in April 2023 to monitor North America, and Europe will be covered by ESA/EUMETSAT Sentinel-4 (Bazalgette Courrèges-Lacoste et al., 2017) in 2025. Common objectives for these missions will provide column products for ozone (${\mathrm{O}}_{\mathrm{3}}$), nitrogen dioxide (${\mathrm{NO}}_{\mathrm{2}}$), sulfur dioxide (${\mathrm{SO}}_{\mathrm{2}}$), formaldehyde (HCHO), and aerosol optical depth, among others, several times per day at 5–10 km px${}^{-\mathrm{1}}$ spatial scales. Together with LEO sensors such as JPSS/CrIS (Joint Polar Satellite System/Cross-track Infrared Sounder) (Han et al., 2013), MetOp/IASI (Infrared Atmospheric Sounding Interferometer) (Clerbaux et al., 2009), and Sentinel-5P/TROPOMI (TROPOspheric Monitoring Instrument) (Veefkind et al., 2012), the new GEO missions will form an atmospheric composition satellite virtual constellation with nearly continuous Northern Hemisphere coverage and unprecedented capability to meet the needs of AQ research and applications (CEOS, 2019).

The hourly measurement time resolution is the novel perspective provided by the new GEO platforms and enables the following: (1) investigations of the diurnal processes determining atmospheric composition; (2) improvements in retrieval sensitivity gained with possible longer measurement acquisition dwell times; (3) high observation data density; and (4) the increased probability of obtaining at least some daily cloud-free observations at any given location (e.g., Fishman et al., 2012; Zoogman et al., 2017; Kim et al., 2020). An important role for the GEO sensors will be in detailing how the new hourly information improves our understanding of diurnal changes that take place with pollutant emissions and subsequent chemistry and transport and how this leads to diurnal changes in AQ. In urban and polluted regions, where these diurnal changes are large, this will likely lead to revised estimates for emissions, population exposure, and associated health and environmental risks compared with those previously based on LEO measurements. This work investigates the processes that drive the diurnal variation in ${\mathrm{NO}}_{\mathrm{2}}$ over Northeast Asia, particularly over Seoul, South Korea, using GEMS data in combination with other satellite and ground-based remote sensing measurements and 3D atmospheric chemical transport model (CTM) analysis.

Of the trace gas products that are routinely retrieved from satellite sensor shortwave spectral measurements, ${\mathrm{NO}}_{\mathrm{2}}$ is one of the most reliable due to the relatively strong signal. It plays a central role in atmospheric chemistry and tropospheric ${\mathrm{O}}_{\mathrm{3}}$ and aerosol formation and is photochemically linked with nitrogen oxide (NO) as reactive nitrogen (${\mathrm{NO}}_x\equiv \mathrm{NO}+{\mathrm{NO}}_{\mathrm{2}}$) (Brasseur et al., 1999). ${\mathrm{NO}}_x$ emissions occur primarily as NO and have anthropogenic sources associated with high-temperature combustion processes in the power, industry, and transport sectors (e.g., Goldberg et al., 2021; de Foy and Schauer, 2022). Natural sources include lightning, biomass burning, and soil emissions, amongst others (e.g., Griffin et al., 2021; Huber et al., 2020). The ${\mathrm{NO}}_{\mathrm{2}}$ minutes-to-hours daytime lifetime in the planetary boundary layer (PBL) also means that it does not become evenly mixed in the atmosphere, and polluted regions, especially urban areas, often show satellite-derived ${\mathrm{NO}}_{\mathrm{2}}$ column enhancements of many times the background level. These products are, therefore, particularly useful for understanding ${\mathrm{NO}}_x$ and other emissions, their subsequent chemical and physical transformations, and attributing pollution trends over time due to emission regulations and other factors such as the COVID pandemic lockdowns and economic downturns (e.g., Duncan et al., 2013; Levelt et al., 2022; de Ruyter de Wildt et al., 2012). This has resulted in a wealth of peer-reviewed literature based on LEO satellite observations detailing pollutant research and AQ applications and management (e.g., Curier et al., 2014; Duncan et al., 2016; Liu et al., 2017). The partitioning of ${\mathrm{NO}}_x$ between NO and ${\mathrm{NO}}_{\mathrm{2}}$ is in a photochemical steady state that establishes on a timescale of minutes during daylight hours. Details are given in Brasseur et al. (1999), and the ${\mathrm{NO}}_x$ ratio can be represented as follows:

1$$\left[\mathrm{NO}\right]/\left[{\mathrm{NO}}_{\mathrm{2}}\right]=j_{{\mathrm{NO}}_{\mathrm{2}}}/\left(k_{{\mathrm{O}}_{\mathrm{3}}}\left[{\mathrm{O}}_{\mathrm{3}}\right]+k_{{\mathrm{HO}}_{\mathrm{2}}}\left[{\mathrm{HO}}_{\mathrm{2}}\right]+k_{{\mathrm{RO}}_{\mathrm{2}}}\left[{\mathrm{RO}}_{\mathrm{2}}\right]\right),$$ where the square brackets denote concentration ($\mathrm{molec}.{\mathrm{cm}}^{-\mathrm{3}}$), $k$ is a bimolecular rate coefficient (${\mathrm{cm}}^{\mathrm{3}}\mathrm{molec}{.}^{-\mathrm{1}}{\mathrm{s}}^{-\mathrm{1}}$), $j$ is the photolysis rate (${\mathrm{s}}^{-\mathrm{1}}$), ${\mathrm{HO}}_{\mathrm{2}}$ is the hydroperoxy radical, and ${\mathrm{RO}}_{\mathrm{2}}$ represents all organic peroxy radicals. The main loss of ${\mathrm{NO}}_x$ is through oxidation to the nitrogen reservoirs nitric acid (${\mathrm{HNO}}_{\mathrm{3}}$) and peroxyacetyl nitrate (PAN). The chemical diurnal cycle is discussed further in Sect. 5.3 based on the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ data and modeling.

Following this introduction (Sect. 1), Sect. 2 describes the remote sensing measurements and the model tools that are used in this work. Section 3 presents the ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation observed by GEMS at different spatial scales for our January and June 2023 study months, and this is compared with ground-based remote sensing measurements over Seoul in Sect. 4. Model analysis and discussion in Sect. 5 considers the various processes (emissions, chemistry, and meteorology) that drive the ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation. Conclusions are presented in Sect. 6.

2 Observations and modeling tools

2.1 GEMS

South Korea's GEMS is the first satellite instrument in the GEO constellation and is monitoring AQ over Asia. GEMS was launched in February 2020 by Arianespace from the French Guiana Space Center. Like the TEMPO instrument, GEMS was built by Ball Aerospace & Technologies Corp. GEMS retrievals of ${\mathrm{O}}_{\mathrm{3}}$, ${\mathrm{NO}}_{\mathrm{2}}$, ${\mathrm{SO}}_{\mathrm{2}}$, HCHO, glyoxal (CHOCHO), and aerosols are derived from ultraviolet–visible (UV–vis) measurements (Kim et al., 2020), and the cloud fraction necessary for data filtering is also available for each observation (Choi et al., 2020; Kim et al., 2024). Each day, the field of regard (FOR) shifts westward with the Sun; this provides measurements over India at the end of the day at the expense of losing coverage over Japan when the solar zenith angles become too large. Total column (TotC), stratospheric column (StrC), and (for some products) tropospheric column (TrC) values are retrieved in up to 10 hourly observations during daytime according to the season with a spatial resolution at Seoul of near 7 $\mathrm{km}$ $\times$ 7.7 $\mathrm{km}$ for gases and cloud, whereas this resolution is 3.5 $\mathrm{km}$ $\times$ 7.7 $\mathrm{km}$ for aerosol and surface reflectivity.

The main retrieval challenges for ${\mathrm{NO}}_{\mathrm{2}}$ (e.g., Palmer et al., 2001; Bucsela et al., 2013; Lorente et al., 2017; Geddes et al., 2018; Zara et al., 2018; van Geffen et al., 2020) are (1) the conversion of the observed ${\mathrm{NO}}_{\mathrm{2}}$ slant column density to an inferred vertical column density using an air mass factor (AMF) and (2) the separation of the StrC and TrC. These steps can both lead to significant uncertainties and biases as is well-documented in studies comparing the varying results from different ${\mathrm{NO}}_{\mathrm{2}}$ retrieval algorithms using the same OMI (Ozone Monitoring Instrument; Levelt et al., 2018) satellite measurements (Zara et al., 2018). The AMF is primarily a geometric conversion based on the observation angles, but this must also consider other factors including cloud and aerosol information, terrain reflectivity, and vertical gas profile (Lorente et al., 2017). A CTM is used in this AMF calculation and also in the separation of the StrC and TrC (Lee et al., 2020; Geddes et al., 2018). The ${\mathrm{NO}}_{\mathrm{2}}$ a priori profiles in this GEMS data version are provided by the GEOS-Chem model (Bey et al., 2001). Although beyond the scope of this paper, retrieval sensitivity studies and product validation for the new GEO composition measurements will be important to minimize any aliasing of diurnal variation in the retrieval input parameters onto the diurnal variation in the retrieved products themselves (e.g., Yang et al., 2023a; Kim et al., 2023; Szykman and Liu, 2023). This includes, for example, parameters that impact the AMF, a priori assumptions that change by location and time; angular dependences of surface and cloud reflectivities; vertical profiles of trace gases and aerosols; meteorology; and PBL evolution.

This work uses the publicly available Version 2 (V2.0) ${\mathrm{NO}}_{\mathrm{2}}$ Level-2 data obtainable from the Korean National Institute of Environmental Research (NIER) (NIER, 2024). An example of the coverage is shown in Fig. 1. The data quality flags used are those recommended by the algorithm development team (Lee et al., 2022). These are as follows: FinalAlgorithmFlags $\le \mathrm{1}$; CloudFraction $<\mathrm{0.3}$; and SolarZenithAngle and ViewingZenithAngle $\le \mathrm{70}$°. The nominal GEMS pre-launch accuracy for the ${\mathrm{NO}}_{\mathrm{2}}$ TrC is $\mathrm{1}\times {\mathrm{10}}^{\mathrm{15}}$ ($\mathrm{molec}.{\mathrm{cm}}^{-\mathrm{2}}$) (Kim et al., 2020). The Level-2 data contain an error term for the ${\mathrm{NO}}_{\mathrm{2}}$ spectral fit, but this does not account for the AMF calculation error and the conversion to vertical column. Analysis for TROPOMI (van Geffen et al., 2022) identified the latter as the main source of product error (around $\pm \mathrm{25}$ % over polluted regions). Comparison of the GEMS L2 ${\mathrm{NO}}_{\mathrm{2}}$ with other measurements is further discussed in Sect. 4. Updates and enhancements to the GEMS retrieval algorithms are ongoing and will result in new data versions being released periodically.

Figure 1

Averaged GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC for June 2023 showing the full extent of the instrument FOR. Blue boxes indicate the Northeast Asia and Seoul study regions; see the text for details. The color discontinuity near 130° E is due to the lower number (4) of daytime hourly observations towards the east compared with the 10 daytime hourly observations in the center of the domain that result from the FOR shifting westward with the Sun during the day.

[Figure omitted. See PDF]

2.2 TROPOMI

TROPOMI is a push-broom imaging spectrometer on ESA's Sentinel-5 Precursor satellite in a Sun-synchronous orbit with a 13:30 local standard time Equator crossing (Veefkind et al., 2012). TROPOMI achieves close-to-global daily coverage at resolutions down to $\mathrm{3.5}\mathrm{km}\times \mathrm{5.5}\mathrm{km}$, depending on the species. ${\mathrm{NO}}_{\mathrm{2}}$, HCHO, carbon monoxide (CO), ${\mathrm{SO}}_{\mathrm{2}}$, ${\mathrm{O}}_{\mathrm{3}}$, methane (${\mathrm{CH}}_{\mathrm{4}}$), aerosol, and cloud are retrieved from UV–vis and reflected shortwave infrared measurements. We use the TROPOMI operational Level-2 data from Collection 3 that are publicly available through the NASA Earthdata portal (Earthdata, 2024).

2.3 NASA–ESA Pandonia Global Network (PGN)

To capture time-resolved measurements of highly variable species such as ${\mathrm{NO}}_{\mathrm{2}}$ in a coordinated manner, NASA initiated a large-scale global monitoring network of (quasi-)autonomous stations with the ground-based remote sensing spectrometer system called Pandora (Herman et al., 2009; Spinei et al., 2018). ESA joined this project in 2018 to form the Pandonia Global Network (PGN), which ensures systematic processing and dissemination of the data in support of AQ monitoring and satellite validation. Pandora instruments measure in the UV–vis range and retrieve column amounts of several air pollutants including ${\mathrm{NO}}_{\mathrm{2}}$, HCHO, and ${\mathrm{O}}_{\mathrm{3}}$. Data are publicly available (PGN, 2024).

2.4 Atmospheric chemistry model framework

The Multi-Scale Infrastructure for Chemistry and Aerosols (MUSICA) is a new community CTM for simulations of large-scale atmospheric phenomena in a global modeling framework, while still resolving chemistry at emission- and exposure-relevant scales (Pfister et al., 2020). In this work, we use MUSICA Version 0 (MUSICAv0), which is a configuration of the Community Atmospheric Model with Chemistry (CAM-chem) (Tilmes et al., 2019; Emmons et al., 2020) using a spectral element (SE) grid with regional refinement (RR) (CAM-chem-SE-RR) (e.g., Lauritzen et al., 2018; Schwantes et al., 2022). MUSICAv0 is run with a horizontal resolution of 0.0625° ($\sim \mathrm{7}$ km) over refined regions selected to cover the Korean and wider Asian domain (Jo et al., 2023) and allows near matching of the GEMS pixel resolution of $\mathrm{7}\mathrm{km}\times \mathrm{7.7}\mathrm{km}$ over Seoul (see Fig. S1 of Jo et al., 2023). Chemical processes are all simulated in CAM-chem, which includes chemistry feedback on the meteorology (e.g., aerosol–cloud interactions). To reproduce the dynamics for the January and June 2023 months analyzed, the capability of CAM-chem to nudge the model meteorology to the GEOS-5 0.25° resolution reanalysis outside of the refined domain is used following Jo et al. (2023). Inside of the refined domain, the wind fields are calculated by the model. We stress that this work does not aim for exact model simulations of the GEMS data. Rather, the model is used to investigate the processes driving the observed ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation.

Date-specific anthropogenic, biomass burning, and biogenic emissions are used in the simulations. The Copernicus Atmospheric Monitoring System version 5.1 (CAMS-GLOB-ANTv5.1) global emission inventory (Soulié et al., 2024) serves as the base anthropogenic emissions inventory along with the NIER/KU-CREATE inventory for East Asia and the Korean Peninsula that was produced for the Korea–United States Air Quality Study (KORUS-AQ) field campaign in May–June 2016 (Jang et al., 2019; Park et al., 2021; Crawford et al., 2021). Global biomass burning emissions are provided as 0.1° resolution daily averages by the Quick Fire Emissions Dataset (QFED) version 2.5_r1 (Koster et al., 2015) and Fire INventory from NCAR (FINN) v1.5 (Wiedinmyer et al., 2011). Biogenic emissions are simulated using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.1 algorithm (Guenther et al., 2012), which is incorporated in the Community Land Model (CLM) (Lawrence et al., 2019) and calculated at each model time step using the model meteorology. Global inventories of anthropogenic emissions are usually provided as monthly means that are temporally interpolated to a particular day by the model. However, the diurnal variation in emissions also becomes important at the high spatial resolution of the MUSICAv0 refined grid. Diurnal emissions profiles have been derived for different sectors by country in the Emissions Database for Global Atmospheric Research (EDGAR) and can be applied to other inventories (Crippa et al., 2020; Jo et al., 2023).

For our simulations over Seoul (presented in Sect. 5.1), we consider the diurnal variation in emissions using profiles based on area–point and mobile sectors developed for KORUS-AQ and described in application to modeling over Seoul by Jo et al. (2023). This is shown in Fig. 2 and predicts a pronounced increase in emissions with daytime activity and rush-hour peaks in the mobile emissions. As is discussed in Sect. 5.1, the assumed diurnal profile of model emissions can make a significant difference to the calculated TrC. Here, we include Fig. 2 showing the shape of the diurnal emissions to help in the interpretation of the results of Sect. 5.1 (particularly Fig. 10).

Figure 2

KORUS-AQ diurnal emissions profiles for the area–point and mobile sectors.

[Figure omitted. See PDF]

Quantitative comparison of satellite retrievals and model simulations requires consideration of the measurement sensitivity to the target quantity and the retrieval a priori assumptions. These can be accounted for by applying the retrieval averaging kernels that formally relate the retrieved quantity to the true atmosphere (Rodgers, 2000). For retrieval–model comparisons of trace gas TrC from GEMS-like sensors, the averaging kernels allow a retrieval-consistent TrC to be derived from the model trace gas profile or, alternatively, a model-consistent TrC to be calculated from the retrieval by substituting the retrieval a priori trace gas profile used in the AMF calculation for the model profile (e.g., Boersma et al., 2016). However, there are known issues with the GEMS V2.0 processing of the ${\mathrm{NO}}_{\mathrm{2}}$ averaging kernels and the values reported in the operational data files, such that these should not be used as per guidance from NIER. The intention of the GEMS project is to remedy this issue with the next GEMS Version 3 release. Any independent or alternative reprocessing of the GEMS data to calculate averaging kernels is beyond the scope of this work. As a result, we are unable to perform quantitative model comparisons of the GEMS V2.0 products at this time, and the comparisons with MUSICAv0 presented in Sect. 5 should be considered mainly qualitative for now.

3 GEMS-observed NO${}_{\mathrm{2}}$ diurnal variation

3.1 Hourly measurements

An example of the 10 GEMS daytime retrievals of ${\mathrm{NO}}_{\mathrm{2}}$ TrC over Northeast Asia (25–45° N, 115–130° E) for the relatively clear day of 15 June 2023 is shown in Fig. 3. High ${\mathrm{NO}}_{\mathrm{2}}$ is seen over the Beijing region and the industrial areas of the North China Plain. Pollution is also high over Shanghai and the Yangtze Delta with another hotspot over Seoul in South Korea. The most striking first impression of this GEO perspective on atmospheric composition is how large the temporal variation in the pollution is with respect to magnitude as well as how much the spatial distribution shifts on an hour-by-hour basis. In certain locations, changing cloud fields do not permit for all 10 possible retrievals to pass the data filters. However, the hourly observations do provide at least some measurements, thereby demonstrating an advantage of the GEO perspective. During the winter months when the Sun is low in the sky, there are fewer GEMS hourly retrievals. An example is shown in Fig. 4 for 30 January 2023 ${\mathrm{NO}}_{\mathrm{2}}$ TrC over Northeast Asia. Compared with Fig. 3 (which uses the same color scale), the ${\mathrm{NO}}_{\mathrm{2}}$ burden is considerably higher because of reduced ${\mathrm{NO}}_x$ photochemical loss and ${\mathrm{NO}}_{\mathrm{2}}$ TrC buildup during the day. This results in less diurnal variation compared with the summer months and is discussed in Sect. 5. The east–west data stripes evident in Fig. 3 exist in this data version across the domain, similar to the spurious across-track variability issue for OMI. Zhang et al. (2023) comment that this is likely associated with the specific scan modes of GEMS as well as periodically occurring bad pixels.

Figure 3

Northeast Asia ${\mathrm{NO}}_{\mathrm{2}}$ TrC hourly values for 15 June 2023. Observation times are Korean standard time (KST), i.e., coordinated universal time (UTC) plus 9 h. Gray indicates that no data were taken during nighttime or missing data due to clouds.

[Figure omitted. See PDF]

Figure 4

Same as Fig. 3 but for 30 January 2023 ${\mathrm{NO}}_{\mathrm{2}}$ TrC.

[Figure omitted. See PDF]

3.2 Quantifying GEMS NO${}_{\mathrm{2}}$ diurnal variation

We have developed quantitative measures of the magnitude of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC daily absolute and relative variation. The first of these – ADV (absolute daily variation) – represents the absolute change in ${\mathrm{NO}}_{\mathrm{2}}$ TrC for a given day and pixel location as sampled by multiple hourly GEMS observations:

2$${\text{ADV}}_{i,j}={\sum }_{k=\mathrm{1}}^{n-\mathrm{1}}\left|({\text{TrC}}_{i,j,k}-{\text{TrC}}_{i,j,k+\mathrm{1}})\right|,$$ where the pixel longitude and latitude are indexed by $i$ and $j$, respectively, and $k$ is the index of the GEMS hourly observation from 1 to $n$, with $n$ being the number of useful cloud-free observations during the day. ADV is calculated for adjacent hourly observations, regardless of gaps due to missing data (gaps along a slope would have no effect on the resulting ADV value, although gaps where the slope changes sign may result in an underestimation). Figure 5 shows the monthly average of ADV for June 2023. Because this quantity depends on the number of useful cloud-free retrievals during the day, it is only calculated for days and locations for which there are at least five hourly observations; thus, it represents a lower bound on the total TrC change. No values are mapped east of $\sim \mathrm{130}$° E, as there are too few daytime hourly observations at these locations. High variation usually coincides with locations of high TrC, such as industrial regions and cities, and illustrates the importance that time-resolved observations will have for characterizing changing ${\mathrm{NO}}_x$ emissions and population pollution exposure. Chinese and Korean cities and Indian power facilities are particularly noticeable. Ship tracks between Hong Kong and Singapore as well as between Sri Lanka and the northern tip of Sumatra have been previously identified in GOME, SCIAMACHY, OMI, and TROPOMI ${\mathrm{NO}}_{\mathrm{2}}$ data, among others (Beirle et al., 2004; Richter et al., 2004; Franke et al., 2009; Georgoulias et al., 2020). The ship tracks in Fig. 5 show diurnal variation, most likely because of horizontal dispersion with the increasing afternoon marine boundary layer. Averaging data temporally in this way at a given location reduces the contribution of transient emission or transport events that can produce significant day-to-day variation in the ${\mathrm{NO}}_{\mathrm{2}}$ TrC. The averaged diurnal variation is then primarily dependent on emission and chemistry processes that occur on most days and, thus, indicates polluted urban regions in particular.

Figure 5

Monthly average of the absolute daily variation (ADV) in the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC for June 2023. The region east of $\sim$ 130° E is not mapped because only data points with five or more observations per day were included in this analysis.

[Figure omitted. See PDF]

A second measure that we consider relates the magnitude of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC relative daily variation (RDV) over multiple hourly observations with respect to the value of the single observation that would be obtained from a LEO instrument such as TROPOMI. For a particular day and pixel location, the absolute deviation in the day's hourly observed ${\mathrm{NO}}_{\mathrm{2}}$ TrC relative to the observation at $t=\text{13:45}$ LT (closest to the TROPOMI overpass time) is calculated and normalized by the $t$ $=$ 13:45 value: 3$${\text{RDV}}_{i,j}={\displaystyle \frac{\mathrm{1}}{{\text{TrC}}_{i,j,t}}}\sqrt{{\displaystyle \frac{{\sum }_{k=\mathrm{1}}^n({\text{TrC}}_{i,j,k}-{\text{TrC}}_{i,j,t}{)}^{\mathrm{2}}}{n}}},$$ where $t$ is the reference time (13:45 local standard time) and $n$ is the number of observations at pixel location $i,j$ excluding the observation acquired at the reference time. The daily RDV can then be averaged over the month, as shown for June 2023 in Fig. 6. The monthly averaged RDV is seen to be large, often $>$ 50 % of the 13:45 LT value. The spatial distribution of RDV is similar to that of ADV shown in Fig. 5; however, in this case, it illustrates the diurnal uncertainties in emissions or exposure that might be expected from assuming estimates based on LEO observations. As for the absolute diurnal variation, because this quantity depends on the number of useful cloud-free retrievals during the day, it again represents a lower bound on the TrC variation. There are also no values at locations such as Japan and India where there is no 13:45 LT observation. The apparent high variation over the Pacific Ocean is a result of normalizing by the relatively low ${\mathrm{NO}}_{\mathrm{2}}$ TrC values in this region.

Figure 6

The June 2023 monthly average of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC relative daily variation (RDV) with respect to the 13:45 LT observed value at each location. Regions west of 90° E and east of $\sim$ 130° E not mapped because there is no observation at 13:45 LT.

[Figure omitted. See PDF]

The January 2023 monthly average of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC ADV and RDV over the Northeast Asia region are shown in Figs. A1 and A2, respectively. In general, reduced winter photochemistry results in a smaller diurnal variation in January compared with June. However, over polluted regions, the ADV in ${\mathrm{NO}}_{\mathrm{2}}$ TrC has similar values due to the higher January TrC. A noticeable region of high absolute daily change is also seen in Cambodia which requires further investigation but may be explained by fires. The Fig. A2 RDV map coverage is limited because the 13:45 LT observation is only available between longitudes 113 and 132° E.

3.3 GEMS regional- and local-scale NO${}_{\mathrm{2}}$ diurnal variation time series

We have examined the time series of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC for various regions and seasons. Figure 7a shows the June 2023 time series spatially averaged over Northeast Asia (see Fig. 1 for the regional context). At this time of year, there are a maximum of 10 daylight hourly data points at the center of the GEMS FOR, with fewer points at the eastern and western edges. The number of hourly data points is further reduced by cloud filtering. On a day-to-day basis, the calculated average TrC appears noisy because of the changing amount and nonuniformity of cloud-free coverage, especially when polluted urban areas might be included in the spatial average on one day but not on the next. Because of this, the monthly time-averaged diurnal variation (Fig. 7b) is the quantity often considered. However, it is important to show the individual daily TrC after filtering for cloudy data to indicate the information that will usually be available for AQ applications. It should also be noted that cloudy missing points in the daily data tend to “flatten out” the apparent diurnal variation shown in the monthly time average; this suggests that this quantity be treated with care, as it may not capture the full dynamic range of the diurnal variation of individual days. Despite these considerations, a consistent diurnal cycle is seen on those days with multiple data points. This shows ${\mathrm{NO}}_{\mathrm{2}}$ TrC decreasing through the morning hours, reaching a minimum in the early afternoon, and then increasing again late in the afternoon. Little difference is seen between weekdays and weekends at this spatial scale. As discussed in Sect. 5, we attribute this summer cycle primarily to photochemistry being the dominant driver of diurnal variation, as diurnal variation due to different local-scale emissions and meteorology is averaged out at this regional scale. This photochemical diurnal cycle is even more apparent when averaging over larger geographical regions, although different local times, solar zenith angles, and photolysis rates then complicate interpretation. A similar diurnal cycle is also seen in clean regions away from local sources or transported pollution plumes.

Figure 7

The June 2023 diurnal ${\mathrm{NO}}_{\mathrm{2}}$ TrC values spatially averaged over (a) the Northeast Asia region and (c) Seoul (see Fig. 1 for the regional context). Depending on cloud cover, there are up to 10 data points each day and no data at night. The black dot each day indicates the value closest to local noon. The monthly average weekday and weekend daily ${\mathrm{NO}}_{\mathrm{2}}$ variation is also shown for (b) the Northeast Asia region and (d) Seoul. Note the high standard deviation (dashed lines).

[Figure omitted. See PDF]

The ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation is less consistent at the local scale where, in addition to photochemistry, changing emissions and, more importantly, meteorology determine day-to-day variability. Figure 7c shows the time series spatially averaged over Seoul, South Korea (37–38° N, 126–127.5° E; see Fig. 1 for the regional context). Prior to the launch of GEMS, stagnation events over Seoul had been seen to cause a buildup of pollution during the day, leading to an afternoon maximum in ${\mathrm{NO}}_{\mathrm{2}}$ TrC. This was observed by the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) aircraft spectrometer (Leitch et al., 2014) during the KORUS-AQ field campaign (Judd et al., 2018; Crawford et al., 2021). GEMS observations sometimes show this same diurnal pattern; however, as discussed in Sect. 5, they more often show a photochemical 10:00–11:00 LT morning maximum in ${\mathrm{NO}}_{\mathrm{2}}$ contributed by urban emissions, followed by a decrease, and then a small late-afternoon increase, as shown in the monthly time-averaged diurnal variation in Fig. 7d. The weekend values indicate a similar diurnal cycle to weekdays with smaller TrC. We note that other work has shown large differences in the ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation seen by GEMS between weekdays and weekends for different Asian cities (J. Park et al., 2022).

Reduced diurnal variation is shown in the January 2023 GEMS time series spatially averaged over the Northeast Asia and Seoul regions, as shown in Fig. 8. As noted above, the apparent large day-to-day differences in TrC result primarily from the varying number of cloud-free data that enter the spatial average, especially for the large-scale Northeast Asia region. Over both regions, an increase in TrC is usually observed during the day because of reduced winter photochemistry, as discussed in Sect. 5. This is the case for both weekdays and weekends, although the weekday TrC values are now clearly greater, indicating the persistence of ${\mathrm{NO}}_{\mathrm{2}}$ resulting from higher weekday ${\mathrm{NO}}_x$ emissions.

Figure 8

Same as Fig. 7 but for January 2023 diurnal ${\mathrm{NO}}_{\mathrm{2}}$ TrC. Depending on cloud cover, there are up to six data points each day and no data at night.

[Figure omitted. See PDF]

4 Pandora measurements in Seoul

The Pandora instruments have emerged as the primary source of ground-based measurements for validation of GEMS ${\mathrm{NO}}_{\mathrm{2}}$ and are also used extensively in TROPOMI validation (Kim et al., 2023). The advantage of these Sun photometers is that they provide column retrievals using the same spectral bands as GEMS and have similar measurement vertical sensitivities. The number of Pandora stations across Asia has been rising rapidly, and there are now 34 instruments contributing to the PGN. Here, we use Pandora measurements in Seoul as an independent indication of the diurnal variation.

For our study months, there were two PGN Pandora instruments making measurements within Seoul, at Seoul National University (Seoul-SNU, Pandora no. 149) close to Gwanaksan, and about 12 km to the north across the city and closer to the center at Yonsei University (Seoul-YN, Pandora no. 54). A comparison of the monthly time-averaged diurnal variation in the ${\mathrm{NO}}_{\mathrm{2}}$ TotC measurements from GEMS and the Pandora instruments is shown in Fig. 9 for weekdays in June and January 2023. Figure 9 also indicates the monthly mean TROPOMI TotC ${\mathrm{NO}}_{\mathrm{2}}$ at the local overpass time. We show Pandora ${\mathrm{NO}}_{\mathrm{2}}$ TotC retrievals that use direct Sun measurements, rather than the multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements used for the TrC estimate. We also note that our model studies with the Whole Atmosphere Community Climate Model (WACCM) indicate that, over relatively polluted regions such as Seoul, the stratosphere contributes only a few percent ${\mathrm{NO}}_{\mathrm{2}}$ to the total column. The GEMS and TROPOMI TotC values are calculated using a mean of retrievals within 5 km of the Pandora sites. For the Pandora–satellite measurement comparisons, we follow previous work (e.g., Judd et al., 2020; Lambert et al., 2023).

Figure 9

GEMS, TROPOMI, and Pandora ${\mathrm{NO}}_{\mathrm{2}}$ TotC values at Seoul-SNU and Seoul-YN, South Korea, for weekdays in June and January 2023. The GEMS and TROPOMI TotC are calculated using a mean of retrievals within 5 km of the Pandora sites.

[Figure omitted. See PDF]

The GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TotC values are larger overall than their Pandora counterparts with a higher bias in January than June. This positive bias has also been identified in comparisons with ground-based DOAS measurements with a median relative difference of $+\mathrm{64}$ % and a correlation coefficient of 0.75 (Lange et al., 2024). This is contrary to what is usually found when comparing pixel-averaged satellite retrievals to local measurements that might capture small-scale high values (e.g., Herman et al., 2019). Tang et al. (2021) show that this representativeness error can account for a single Pandora measurement in Seoul being as much as $\sim \mathrm{25}$ % higher than the GEMS retrieval for the corresponding pixel. Indeed Kim et al. (2023) found that GEMS V1.0 ${\mathrm{NO}}_{\mathrm{2}}$ TotC measurements tend to be lower than their Pandora counterparts at less-polluted sites south of Seoul. Our own analysis at other Korean Pandora sites shows a much lower GEMS–Pandora bias in clean regions along with a small or flat diurnal variation that was also reported by Lange et al. (2024). We find that the GEMS V2.0 data positive bias is less than that of the previous V1.0 data but may still indicate retrieval issues to be addressed in future GEMS data releases. (A limited preview of the upcoming GEMS V3.0 ${\mathrm{NO}}_{\mathrm{2}}$ data release with improved AMF calculation and StrC–TrC separation shows closer agreement with TROPOMI in summer but still an overestimation in winter.) The agreement between Pandora and TROPOMI is good at both sites and for both months, even though large negative (0 % to $-\mathrm{50}$ %) biases in TROPOMI ${\mathrm{NO}}_{\mathrm{2}}$ TotC have previously been reported (Verhoelst et al., 2021). We note here that we find no obvious measurement local time dependence in the GEMS–Pandora bias for Seoul or for the other Korean sites that we have examined.

Despite the positive GEMS bias, the agreement in the pattern of ${\mathrm{NO}}_{\mathrm{2}}$ TotC diurnal variation captured by the Pandora instruments and the corresponding GEMS observations is reasonable at both Seoul sites in both months. The column amounts are lower in June than in January and indicate a morning ${\mathrm{NO}}_{\mathrm{2}}$ maximum followed by a decrease through early afternoon and then a slight increase in the late afternoon. A clear rush-hour peak is not seen in these TotC measurements and is discussed further in Sect. 5.2. The sharp gradient in GEMS data over Seoul-YN in June between 12:00 and 13:00 LT is due to the value at 13:00 LT being anomalously low. This occurs because of the limited number of measurements (two or three per day) that meet the coincidence criteria with Pandora as well as the low number of cloud-free days in this month. The weekend values (not shown) have a flatter diurnal profile and less variation within each hour, although the average magnitude is only slightly smaller than weekdays. In January, both sites show a flat or increasing TrC during the day.

The Pandora ${\mathrm{NO}}_{\mathrm{2}}$ TotC values are generally higher at Seoul-YN in June under the prevailing wind from the south at this time of year that blows more pollution from the city center toward this Pandora site. The opposite occurs during January when the prevailing wind is from the northeast. The same finding for Pandora–TROPOMI comparisons was previously reported by J.-U. Park et al. (2022). The GEMS satellite data capture some of this difference between the two sites and suggest that more work is needed to assess the capability the of GEMS to resolve pixel-scale urban variability for AQ applications.

5 Model studies and discussion

5.1 Model NO${}_{\mathrm{2}}$ diurnal variation

We have used the MUSICAv0 model to simulate the ${\mathrm{NO}}_{\mathrm{2}}$ TrC for the same Northeast Asia and Seoul regions discussed above for June and January of 2023, and the model setup is as described in Sect. 2.4. For each case, we performed two simulations as a sensitivity test of the assumed anthropogenic diurnal emissions profile: the first assumes constant anthropogenic emissions during the day (labeled “Base”), whereas the second uses the KORUS-AQ area–point and mobile sector diurnal emissions profiles described in Sect. 2.4 and shown in Fig. 2 (labeled “Diurnal”). The average daily ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation for weekdays is shown in Fig. 10. The time windows corresponding to the periods during which GEMS retrievals are shown in Figs. 7 and 8 are indicated by the unshaded hours. As is noted in Sect. 2.4, there is an issue with the GEMS V2.0 ${\mathrm{NO}}_{\mathrm{2}}$ averaging kernels which should not be used following the guidance of NIER. As a result, the discussion here comparing MUSICAv0 simulations to the GEMS retrievals presented in Sect. 3.3 should be considered to be mainly qualitative. However, we believe that the model is still able to provide insights into the drivers of the ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation and that presentation of these results is worthwhile as it sets the stage for more quantitative analysis with the next GEMS data version.

Figure 10

MUSICAv0 ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation averaged over the Northeast Asia region (a, c) and Seoul (b, d) for weekdays in June 2023 (a, b) and January 2023 (c, d). The time windows corresponding to the periods during which GEMS retrievals are shown in Figs. 7 and 8 are indicated by the unshaded hours. The Base simulation assumes a constant diurnal emissions profile, whereas the Diurnal simulation uses the diurnal emissions profile discussed in Sect. 2.4 and shown in Fig. 2.

[Figure omitted. See PDF]

5.2 The role of emissions

We first consider the model results spatially averaged over the Northeast Asia study region and temporally averaged for weekdays in the months of June and January 2023 (Fig. 10a and c, respectively). The model patterns for the ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation are generally similar to the corresponding GEMS (Figs. 7b and 8b). However, as the model does not include cloud cover and reproduces a consistent daily pattern, the dynamic range of the diurnal variation is greater than the averaged GEMS data. The GEMS data are biased high relative to the model in both months with the difference being largest in January. This may be a GEMS V2.0 retrieval issue, as noted above, and/or an underestimation of ${\mathrm{NO}}_x$ emissions in MUSICAv0. At this regional scale, the diurnal variation is similar for both the Base and Diurnal simulations, despite the very different anthropogenic diurnal emissions profiles, and suggests that photochemistry is the main driver. However, the magnitude of the diurnal variation during the GEMS retrieval window does depend on the emissions profile, ranging from 25 %–50 % in June to 6 %–17 % in January. We note that the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC daily relative variation over Northeast Asia for June and January 2023 (Figs. 6 and A2, respectively) falls at the bottom of these ranges, suggesting that further work is needed to understand the greater model variation. The emissions profile also affects the local hour of the minimum ${\mathrm{NO}}_{\mathrm{2}}$ TrC and is about 1 h earlier in the Diurnal case, which matches better with GEMS.

Over the Seoul region, the difference in ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation between the Base and Diurnal simulations is large, especially in June (Fig. 10b). For the Base simulation in June, the shape of the diurnal variation shows a similar photochemical cycle to that described above for the Northeast Asia region. A difference is seen in that there is a ${\mathrm{NO}}_{\mathrm{2}}$ morning peak around 10:00–11:00 LT, a decrease through midday, and then an afternoon minimum, like the diurnal variation seen in the corresponding GEMS data (Fig. 7d). This is discussed further in the next section. In contrast, the Diurnal simulation shows a very different ${\mathrm{NO}}_{\mathrm{2}}$ buildup throughout the day and reflects the shape of the ${\mathrm{NO}}_x$ diurnal emissions profile used in the model; however, the pronounced rush-hour peaks seen in Fig. 2 are not evident in the ${\mathrm{NO}}_{\mathrm{2}}$ TrC average, even though they do clearly show up in the calculated surface concentration (not shown). Nevertheless, the fact that the average GEMS ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation shown in Fig. 7d more closely resembles the constant ${\mathrm{NO}}_x$ emissions of the Base simulation suggests that the hourly changes in the emissions profile may not actually be as large as indicated in Fig. 2. In January, both simulations show increasing TrC during the GEMS retrieval window, in agreement with the GEMS data in Fig. 8d. These simulations indicate that the modeled diurnal variation at the city scale will be very dependent on the diurnal profile of the assumed emissions, and the accurate characterization of the assumed emissions will be an important part of effectively using the hourly observations from GEO.

5.3 The role of chemistry

The underlying photochemical cycle is explained by considering the June 2023 Base simulation TrC ${\mathrm{NO}}_x$ budget analysis shown in Fig. 11a for Seoul. After a buildup of ${\mathrm{NO}}_{\mathrm{2}}$ during nighttime, photolysis begins with sunrise at 05:00 LT, and the morning decrease is mirrored by the increase in NO with the rise in the ${\mathrm{NO}}_{\mathrm{2}}$ photolysis rate ($j_{{\mathrm{NO}}_{\mathrm{2}}}$) curve with increasing solar elevation. Although the ${\mathrm{NO}}_x$ ratio is in a photochemical steady state on timescales of minutes, the diurnal change in solar irradiance drives a continual change in the ratio through the day. It should also be remembered that this calculation is for the TrC relevant to the GEMS retrieval and is, therefore, representative of a vertically weighted average, rather than surface values. After 10:00 LT, NO loss with the buildup of ${\mathrm{O}}_{\mathrm{3}}$ pushes the ${\mathrm{NO}}_x$ ratio toward ${\mathrm{NO}}_{\mathrm{2}}$ at the same time that ${\mathrm{NO}}_x$ is lost to the nitrogen reservoirs ${\mathrm{HNO}}_{\mathrm{3}}$, and to a lesser extent, PAN. This balance results in a slight peak in ${\mathrm{NO}}_{\mathrm{2}}$ around 11:00 LT; a continued decline due to ${\mathrm{NO}}_x$ loss is then seen until the minimum at 16:00 LT. Decreasing photolysis results in a subsequent ${\mathrm{NO}}_{\mathrm{2}}$ increase into nighttime with continuing ${\mathrm{NO}}_x$ emissions. The underlying chemistry is similar for the corresponding June Diurnal simulation (not shown) only in this case, as the increasing ${\mathrm{NO}}_x$ emissions during the day result in the replacement of most of the atmospheric ${\mathrm{NO}}_x$ that is lost to the nitrogen reservoirs with a consequent gradual buildup of ${\mathrm{NO}}_{\mathrm{2}}$.

Figure 11

MUSICAv0 (a) June and (b) January 2023 average daily ${\mathrm{NO}}_x$ TrC budget analysis over Seoul assuming constant anthropogenic emissions during the day (Base simulation). The time windows corresponding to the periods during which GEMS retrievals are shown in Figs. 7 and 8 are indicated by the unshaded hours.

[Figure omitted. See PDF]

In the January 2023 Base simulation shown in Fig. 11b, atmospheric ${\mathrm{NO}}_x$ shows less variation under conditions of lower photolysis, lower ${\mathrm{O}}_{\mathrm{3}}$, and less daytime conversion to ${\mathrm{HNO}}_{\mathrm{3}}$. The ${\mathrm{NO}}_x$ ratio depends mainly on the change in the $j_{{\mathrm{NO}}_{\mathrm{2}}}$ curve under conditions of limited photochemical activity, resulting in a 11:00 LT NO maximum and ${\mathrm{NO}}_{\mathrm{2}}$ minimum (as shown in Fig. 10d). In the January Diurnal simulation (not shown), ${\mathrm{NO}}_{\mathrm{2}}$ again builds up during the day following the increasing ${\mathrm{NO}}_x$ emissions.

5.4 The role of meteorology

The MUSICAv0 monthly averaged daily ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation over Seoul (shown in Fig. 10b and d) results from averaging the diurnal variation during each day of June and January 2023. As noted before, the Base simulation usually shows an afternoon minimum, whereas the Diurnal simulation shows a buildup of pollution during the day and produces a late-afternoon ${\mathrm{NO}}_{\mathrm{2}}$ peak. Both simulations indicate that day-to-day changes in the ${\mathrm{NO}}_{\mathrm{2}}$ TrC magnitude depend primarily on meteorology. This is illustrated in Fig. 12, which shows the correlation of the average model ${\mathrm{NO}}_{\mathrm{2}}$ TrC during the day with the model surface-layer wind (usually around $\sim$ 120 m) and also indicates the average wind direction over the city. This is shown separately in plots for June and January calculations for the 2 years of 2022 and 2023 together. For both months, there is a clear anticorrelation ($R$ about $-\mathrm{0.7}$) of ${\mathrm{NO}}_{\mathrm{2}}$ TrC and wind speed. In June, the prevailing wind over Seoul is usually from the south. Shifts to winds from the west result in a lower wind speed and stagnant conditions over the city that permit a buildup of pollution, and this is reflected in the TrC value. Similar anticorrelation is seen in January, although the winds over Seoul during winter are mostly from the northwest and an occasional change to a weak anticyclonic pattern results in a low wind speed and ${\mathrm{NO}}_{\mathrm{2}}$ TrC buildup.

Figure 12

Scatter plot of the MUSICAv0 average daily ${\mathrm{NO}}_{\mathrm{2}}$ TrC compared to the magnitude and direction of the prevailing model surface-layer wind (usually around $\sim \mathrm{120}$ m) over Seoul for June and January combined data for the 2 years of 2022 and 2023 assuming constant anthropogenic emissions during the day (Base case).

[Figure omitted. See PDF]

A similar analysis for the modeled diurnal variation absolute daily change in ${\mathrm{NO}}_{\mathrm{2}}$ TrC discussed in Sect. 3.2 does not show clear correlation ($R$ of about $-\mathrm{0.35}$) with wind speed in either month over Seoul. This suggests that the emission and chemistry processes discussed above are most important in determining the local diurnal variation and do not necessarily require stagnant meteorological conditions. We note that Yang et al. (2023b) did see correspondence between a low model wind speed and higher winter ${\mathrm{NO}}_{\mathrm{2}}$ diurnal variation; therefore, further investigation using wind measurements would be useful. We have also examined the contribution of incoming transport to the ${\mathrm{NO}}_{\mathrm{2}}$ TrC, although Seoul is unlikely to be a generally representative city in this respect, as it is large, fairly isolated, and has very high local ${\mathrm{NO}}_x$ emissions. Upwind GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC values are usually about 3–4 times lower than the values retrieved over the city and have relatively flat diurnal variation, suggesting that incoming transport contributions to the Seoul ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal or day-to-day variations will be small. This might not be the case for longer-lived pollutants or when considering concentrations at a specific altitude, such as in the free troposphere (Jordan et al., 2020). In cleaner regions downwind of Seoul, the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC measurements do show higher values because of plumes following a high-pollution day over the city. Understanding the role of meteorology is going to be important for our next step of relating the satellite retrievals of TrC to surface concentrations. Results from KORUS-AQ showed that meteorology and PBL dynamics play a large role in determining the extent to which the satellite and ground-based in situ views of pollutant diurnal variation can be reconciled (Crawford et al., 2021).

6 Conclusion

Over the last 20 years, LEO observations have provided satellite measurements of pollutants in the atmosphere with increasing scientific utility, mainly at continental to global and weekly to seasonal scales. New-generation LEO instruments (e.g., IASI, CrIS, and TROPOMI) have allowed for refinements in both the spatial and temporal resolutions, to city and daily scales. The GEO satellite perspective, with hourly high-spatial-resolution measurements, represents another major step forward, especially with respect to the capability to understand how AQ processes change diurnally at the local scale. The main conclusions of this work are as follows:

• GEMS observations show that ${\mathrm{NO}}_{\mathrm{2}}$ TrC diurnal variation can be large ($>\mathrm{50}$ % of the TrC) and varies by location, being higher in polluted environments. The ${\mathrm{NO}}_{\mathrm{2}}$ distribution is seen to change hourly and can be quite different from what would be seen in a once-a-day LEO observation. This is demonstrated by the quantitative measures of diurnal variation that we have presented, such as the monthly average of the absolute daily change in TrC or the diurnal relative variation in TrC. Along with enabling one or more observations within a day under changing cloud conditions, this demonstrates the advantages of the GEO perspective.

• Regionally averaging GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC data emphasizes the diurnal variation due to chemistry, as local-scale variability due to emissions and meteorology is minimized. Temporally averaging the data for a particular hour over several days or weeks emphasizes persistent chemistry and emissions patterns while minimizing meteorological variability.

• In June, ${\mathrm{NO}}_{\mathrm{2}}$ photochemistry is an important driver of diurnal variation, especially at the regional scale. At the local scale, the ${\mathrm{NO}}_{\mathrm{2}}$ magnitude and diurnal variation patterns change on a day-to-day basis, showing the impact of emissions and meteorology. In January, ${\mathrm{NO}}_{\mathrm{2}}$ columns are higher and diurnal variation is lower because of reduced photochemistry.

• Initial comparisons with Pandora measurements over Seoul show a reduction in the GEMS V2.0 positive bias with respect to GEMS V1.0 and reasonable agreement with respect to the shape of diurnal variation. The GEMS differences between the two Pandora sites suggest the possibility of resolving pixel-scale urban variation for AQ applications.

• Model simulations show high sensitivity to the assumed diurnal emissions profile, especially at the local scale. This will have consequences ranging from the assumed ${\mathrm{NO}}_{\mathrm{2}}$ vertical profile used in retrieval AMF calculations to the background model field used for GEMS data assimilation.

• The model indicates an anticorrelation between the surface-layer wind speed and the daily mean ${\mathrm{NO}}_{\mathrm{2}}$ TrC, the latter of which can build up under stagnant conditions.

This work has concentrated on understanding the diurnal variation in the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC retrievals with a CTM. In combination with ground-based remote sensing and in situ measurements, the next step will be to connect the GEO and LEO satellite-derived columns (not only of ${\mathrm{NO}}_{\mathrm{2}}$ but also other trace gas species, particularly ${\mathrm{O}}_{\mathrm{3}}$ and HCHO) to the surface-level concentrations. This will allow the derivation of top-down diurnal emissions profiles that can be applied to the standard bottom-up emissions inventories. Including this diurnal variation is going to be important for determining true pollutant exposure levels for AQ studies. The work presented here also provides a path for investigating similar ${\mathrm{NO}}_{\mathrm{2}}$ diurnal cycles in the new TEMPO data over North America, and later over Europe with Sentinel-4.

Appendix A

Figure A1

Monthly average of the absolute daily variation (ADV) in the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC for January 2023. Data points with three or more observations per day were included in this analysis.

[Figure omitted. See PDF]

Figure A2

The January 2023 monthly average of the GEMS ${\mathrm{NO}}_{\mathrm{2}}$ TrC relative daily variation (RDV) with respect to the 13:45 LT observed value at each location. Regions west of about 113° E and east of about 132° E are not mapped due to the lack of observations at 13:45 LT.

[Figure omitted. See PDF]

Code availability

MUSICAv0 (Pfister, 2020) is a configuration of CESM2.2, which is an open-source community model available from https://github.com/ESCOMP/CESM (last access: 14 February 2024). The code version used for this work and the modifications to include the diurnal cycle of anthropogenic emissions, grid information files, and simulation results are available at 10.5281/zenodo.8044736 (Jo, 2023).

Data availability

The GEMS L2 ${\mathrm{NO}}_{\mathrm{2}}$ V2.0 data (NIER, 2024) can be obtained via application to NIER (https://nesc.nier.go.kr/en/html/cntnts/91/static/page.do). The Pandora ${\mathrm{NO}}_{\mathrm{2}}$ data are available from the Pandonia Global Network data archive (http://data.pandonia-global-network.org/; PGN, 2024). The TROPOMI ${\mathrm{NO}}_{\mathrm{2}}$ data are publicly available from the NASA Earthdata portal (https://search.earthdata.nasa.gov/; Earthdata, 2024).

Author contributions

DPE: conceptualization; DPE, SMA, DSJ, and IO: methodology; DPE, SMA, DSJ, and IO: formal analysis; SMA, DSJ, IO, HL, JP, and HH: data curation; SMA, IO, JK, HL, JP, and HH: validation; SMA, DSJ, and IO: visualization; DPE, HMW, and LKE: supervision; DPE and SMA: writing – original draft preparation; SMA, DSJ, IO, LKE, JJO, HMW, JK, HL, JP, and HH: writing – review and editing.

Competing interests

At least one of the (co-)authors is a member of the editorial board of Atmospheric Chemistry and Physics. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.

Disclaimer

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Regarding the maps used in this paper, please note that Figs. 1, 5, 6, A1, and A2 contain disputed territories.

Special issue statement

This article is part of the special issue “GEMS: first year in operation (AMT/ACP inter-journal SI)”. It is not associated with a conference.

Acknowledgement

This material is based upon work supported by the National Center for Atmospheric Research, which is a major facility sponsored by the National Science Foundation (under cooperative agreement no. 1852977). Duseong S. Jo was supported by the New Faculty Startup Fund from Seoul National University. The authors would like to acknowledge high-performance computing support from Cheyenne (10.5065/D6RX99HX; Computational and Information Systems Laboratory, 2019), provided by NSF NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation. We are also grateful to Doug Kinnison (NCAR/ACOM) for providing the WACCM simulations and discussing stratospheric ${\mathrm{NO}}_{\mathrm{2}}$. Finally, we wish to thank the TROPOMI and PGN teams for observational data.

Financial support

This research has been supported by the Smithsonian Astrophysical Observatory (grant no. SV3-83021).

Review statement

This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.