text:
GEMS MISSION
User requirements, instrument design, and operation.
DATA PROCESSORS AND SCIENTIFIC DATA PRODUCTS
Commonalities for L2 algorithm.
Tropospheric and total O3.
Nitrogen dioxide (NO2).
Sulfur dioxide (SO2).
HCHO and CHOCHO.
Aerosols.
Clouds.
Surface reflectivity.
UVI.
Algorithm tests.
Validation plan
DATA APPLICATION AND SERVICES
Top-down emission estimates.
Chemical weather forecasts and data assimilation.
Public service.
Geostationary AQ constellation
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Development of satellite remote sensing instruments for atmospheric composition measurements with respect to temporal and spatial resolution. Numbers in parentheses for LEO instruments represent revisit time per location in days. Symbols in squares and circles represent aerosols, and AQ-related trace gases, respectively. Symbol colors represent wavelength ranges, as in the legend. For planned missions, mission names are in italics and symbol outlines are in dashed lines.
(top) Diurnal variation of aerosol optical depth (AOD) at 0900, 1100, 1300, and 1500 KST for May, averaged over 2011–16 from the Geostationary Ocean Color Imager (GOCI). Yonsei Aerosol Retrieval (YAER) algorithm, version 2, was used (M. Choi et al. 2018). (bottom) An example of diurnal variation of trace gas concentrations in addition to AOD for a representative long-range transport event in South Korea on 25 May 2016 during the KORUS-AQ campaign. Values are averaged over the domain of South Korea. Red and blue circles represent satellite measurements of AOD from GOCI (GEO) and MODIS (LEO)—Aqua and Terra. Triangles represent the gas concentration calculated from CMAQ model, with colors for different gases. Background shadows in light blue indicate overpass time of LEO satellites (MODIS Aqua and Terra), and those in light red indicate additional measurement time from GEO satellite.
Optical depth spectra of aerosols and trace gases in the GEMS spectral range for typical GEO measurement geometry. Different colors represent species for vertical optical depths (line) and fitting window/wavelengths used for retrieval (horizontal bar).
(top) GEMS flight model drawing, and pictures of the calibration assembly with (bottom left) aperture, and (bottom right) radiator side view of the instrument. Total mass is 159 kg. Volume is 1,004 mm × 1,088 mm × 865 mm.
The E–W scan scenarios of GEMS, nominal daily scan (yellow), full central scan (green), and full west scan (magenta), within the field of regard (FOR; orange). Background colors represent average NO2 column densities measured by OMI over 2005–14. GEMS line-of-sight (LOS) center, major cities, and N–S spatial resolutions are shown together.
Retrieved results for GEMS algorithms using OMI L1b data for year 2005 (a) tropospheric O3, (b) total O3, (c) NO2, (d) SO2, (e) HCHO, (f) AOD, (g) UV index, (h) surface reflectance, (i) effective cloud fraction (ECF), and (j) cloud centroid pressure (CCP). Each subset consists of (left) annual averages and (right) validation results by comparing the GEMS algorithm results in the ordinates, compared to ozonesondes, Brewer spectrophotometers, OMI operational products, and AERONET in abscissas.
Validation network for aerosols and trace gases within GEMS domain.
The Geostationary Air Quality Constellation, covering most polluted regions in the Northern Hemisphere. The background image is 10-yr average NO2 column densities observed by OMI from 2005 to 2014, showing spatial coverage of GEMS over Asia, TEMPO over North America, and Sentinel-4 over Europe.
New Era of Air Quality Monitoring from Space: Geostationary Environment Monitoring Spectrometer (GEMS)
Jhoon Kim1, Ukkyo Jeong2, Myoung-Hwan Ahn3, Jae H. Kim4, Rokjin J. Park5, Hanlim Lee6, Chul Han Song7, Yong-Sang Choi8, Kwon-Ho Lee9, Jung-Moon Yoo10, Myeong-Jae Jeong11, Seon Ki Park12, Kwang-Mog Lee13, Chang-Keun Song14, Sang-Woo Kim15, Young Joon Kim16, Si-Wan Kim17, Mijin Kim18, Sujung Go19, Xiong Liu20, Kelly Chance20, Christopher Chan Miller20, Jay Al-Saadi21, Ben Veihelmann22, Pawan K. Bhartia23, Omar Torres23, Gonzalo González Abad24, David P. Haffner25, Dai Ho Ko26, Seung Hoon Lee26, Jung-Hun Woo27, Heesung Chong28, Sang Seo Park29, Dennis Nicks30, Won Jun Choi31, Kyung-Jung Moon31, Ara Cho31, Jongmin Yoon31, Sang-kyun Kim31, Hyunkee Hong32, Kyunghwa Lee33, Hana Lee34, Seoyoung Lee34, Myungje Choi34, Pepijn Veefkind35, Pieternel F. Levelt35, David P. Edwards36, Mina Kang37, Mijin Eo37, Juseon Bak38, Kanghyun Baek39, Hyeong-Ahn Kwon40, Jiwon Yang41, Junsung Park41, Kyung Man Han42, Bo-Ram Kim43, Hee-Woo Shin44, Haklim Choi45, Ebony Lee46, Jihyo Chong47, Yesol Cha48, Ja-Ho Koo49, Hitoshi Irie50, Sachiko Hayashida51, Yasko Kasai52, Yugo Kanaya53, Cheng Liu54, Jintai Lin55, James H. Crawford56, Gregory R. Carmichael57, Michael J. Newchurch58, Barry L. Lefer59, Jay R. Herman60, Robert J. Swap61, Alexis K. H. Lau62, Thomas P. Kurosu63, Glen Jaross64, Berit Ahlers65, Marcel Dobber66, C. Thomas McElroy67, and Yunsoo Choi68
1 Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
| 2 Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea, and NASA Goddard Space Flight Center, Greenbelt, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
| 3 Ewha Womans University, Seoul, South Korea
| 4 Pusan National University, Busan, South Korea
| 5 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
| 6 Pukyong National University, Busan, South Korea
| 7 Gwangju Institute of Science and Technology, Gwangju, South Korea
| 9 Gangneung Wonju National University, Gangneung, South Korea
| 10 Ewha Womans University, Seoul, South Korea
| 11 Gangneung Wonju National University, Gangneung, South Korea
| 13 Kyungpook National University, Daegu, South Korea
| 14 Ulsan National Institute of Science and Technology, Ulsan, South Korea
| 15 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
| 16 Gwangju Institute of Science and Technology, Gwangju, South Korea
| 17 Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea
| 18 Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea, and Universities Space Research Association, Columbia, and NASA Goddard Space Flight Center, Greenbelt, Maryland
| 20 Harvard–Smithsonian Center for Astrophysics, Cambridge, Massachusetts
| 21 NASA Langley Research Center, Hampton, Virginia
| 22 ESTEC, ESA, Noordwijk, Netherlands
| 23 NASA Goddard Space Flight Center, Greenbelt, Maryland
| 25 Science Systems and Applications Inc., Lanham, Maryland
| 26 Korea Aerospace Research Institute, Daejeon, South Korea
| 27 Konkuk University, Seoul, South Korea
| 29 Department of Atmospheric Sciences, Yonsei University, Seoul, and Ulsan National Institute of Science and Technology, Ulsan, South Korea
| 30 Ball Aerospace and Technology Corp., Boulder, Colorado
| 31 National Institute of Environmental Research, Incheon, South Korea
| 32 Pukyong National University, Busan, and National Institute of Environmental Research, Incheon, South Korea
| 33 Gwangju Institute of Science and Technology, Gwangju, and National Institute of Environmental Research, Incheon, South Korea
| 35 Royal Netherlands Meteorological Institute (KNMI), De Bilt, and Delft University of Technology, Delft, Netherlands
| 36 National Center for Atmospheric Research, Boulder, Colorado
| 38 Pusan National University, Busan, South Korea, and Harvard–Smithsonian Center for Astrophysics, Cambridge, Massachusetts
| 39 Pusan National University, Busan, South Korea
| 41 Pukyong National University, Busan, South Korea
| 43 Ewha Womans University, Seoul, and Korea Aerospace Research Institute, Daejeon, South Korea
| 50 Chiba University, Chiba, Japan
| 51 Nara Women's University, Nara, and Research Institute for Humanity and Nature, Kyoto, Japan
| 52 National Institute of Information and Communication Technology, Tokyo, Japan
| 53 Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
| 54 University of Science and Technology of China, Hefei, China
| 55 Laboratory for Climate and Ocean-Atmosphere Studies, Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, Beijing, China
| 57 University of Iowa, Iowa City, Iowa
| 58 University of Alabama in Huntsville, Huntsville, Alabama
| 59 NASA Headquarters, Washington, D.C.
| 60 Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, Maryland
| 62 Hong Kong University of Science and Technology, Kowloon, Hong Kong
| 63 Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California
| 66 EUMETSAT, Darmstadt, Germany
| 67 York University, Toronto, Ontario, Canada
| 68 University of Houston, Houston, Texas
https://doi.org/10.1175/BAMS-D-18-0013.1
E1–E22
The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled for launch in February 2020 to monitor air quality (AQ) at an unprecedented spatial and temporal resolution from a geostationary Earth orbit (GEO) for the first time. With the development of UV–visible spectrometers at sub-nm spectral resolution and sophisticated retrieval algorithms, estimates of the column amounts of atmospheric pollutants (O3, NO2, SO2, HCHO, CHOCHO, and aerosols) can be obtained. To date, all the UV–visible satellite missions monitoring air quality have been in low Earth orbit (LEO), allowing one to two observations per day. With UV–visible instruments on GEO platforms, the diurnal variations of these pollutants can now be determined. Details of the GEMS mission are presented, including instrumentation, scientific algorithms, predicted performance, and applications for air quality forecasts through data assimilation. GEMS will be on board the Geostationary Korea Multi-Purpose Satellite 2 (GEO-KOMPSAT-2) satellite series, which also hosts the Advanced Meteorological Imager (AMI) and Geostationary Ocean Color Imager 2 (GOCI-2). These three instruments will provide synergistic science products to better understand air quality, meteorology, the long-range transport of air pollutants, emission source distributions, and chemical processes. Faster sampling rates at higher spatial resolution will increase the probability of finding cloud-free pixels, leading to more observations of aerosols and trace gases than is possible from LEO. GEMS will be joined by NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) and ESA's Sentinel-4 to form a GEO AQ satellite constellation in early 2020s, coordinated by the Committee on Earth Observation Satellites (CEOS).
© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
Corresponding author: Jhoon Kim, jkim2@yonsei.ac.kr
Supplemental material: https://doi.org/10.1175/BAMS-D-18-0013.2.
Air pollution is increasingly understood to be a global issue, requiring an understanding of pollution sources, transport, and transformation from local to regional to global scales (IPCC 2013). Polluting gases such as ozone (O3; see appendix for acronyms), and aerosols, particularly fine particulate matter (PM2.5), are known to be major risk factors for public health (Cohen et al. 2017; Brauer et al. 2016). Half of the global population lives in Asia, and is exposed to high levels of air pollution. This fact has led to increased interest in regional air quality (AQ). Thus, systematic observations of ozone, aerosols, and their precursors [nitrogen dioxide (NO2), sulfur dioxide (SO2), formaldehyde (HCHO), etc.] over wide areas, together with meteorological observations, are critical to public health and environmental policy in this region.
Monitoring AQ from satellites has played a key role in understanding the status of air pollution loadings and trends on the regional to global scale, by providing information on pollutant amounts, emission, and transport in a quantitative manner (e.g., Levelt et al. 2018). Figure 1 summarizes the capabilities of satellite instruments measuring atmospheric composition using remote sensing with respect to temporal and spatial resolution. In the late 1970s, total O3 was measured successfully by the Solar Backscatter Ultraviolet radiometer (SBUV) and the Total Ozone Mapping Spectrometer (TOMS) (Heath et al. 1975). Technology has since advanced to measure important tropospheric trace gas concentrations [O3, NO2, SO2, HCHO, carbon monoxide (CO)] by a number of satellite sensors including the Global Ozone Monitoring Experiment (GOME) 1 and 2 (Burrows et al. 1993; Munro et al. 2016), the Ozone Monitoring Instrument (OMI) (Levelt et al. 2018), the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) (Bovensmann et al. 1999), the Ozone Mapping Profiler Suite (OMPS) (Flynn et al. 2014), and the Tropospheric Monitoring Instrument (TROPOMI) (Veefkind et al. 2012), among others (see supplement for further details).
Citation: Bulletin of the American Meteorological Society 101, 1; 10.1175/BAMS-D-18-0013.1
In addition to a growing number of target species, from just O3 (TOMS) to the multiple trace gas species (GOME, SCIAMACHY, and OMI), spatial resolution has improved from the 100 km scale (GOME) to resolutions of just a few km (TROPOMI), and temporal resolution has improved from days to twice daily (measured by IR). The OMI dataset has been analyzed extensively to understand AQ around the globe, and to derive emission sources (e.g., Levelt et al. 2018; Duncan et al. 2016; McLinden et al. 2016). Using SCIAMACHY and OMI for morning and afternoon orbit measurements, respectively, Boersma et al. (2008) demonstrated the importance of multiple satellite measurements in a single day for improving model accuracy.
Aerosol properties have been observed extensively by a number of LEO satellite instruments, including Moderate Resolution Imaging Spectroradiometer (MODIS; Levy et al. 2013) and Visible Infrared Imaging Radiometer Suite (VIIRS; Jackson et al. 2013). High-temporal- and high-spatial-resolution observations of aerosol properties have been available from geostationary Earth orbit (GEO) instruments: the Meteorological Imager (MI) and the Geostationary Ocean Color Imager (GOCI) on board the Geostationary Korea Multi-Purpose Satellite (GK)-1, also known as Communication, Oceanography and Meteorology Satellite (COMS), and, more recently, from the AHI over Asia (Kim et al. 2008; Kim et al. 2016; Choi et al. 2016; Choi and Ho 2015; Lim et al. 2018). Long-term validation of the GOCI aerosol optical depth (AOD) indicates good agreement with ground-based Aerosol Robotic Network (AERONET) measurements, with correlation coefficients of ∼0.9 (M. Choi et al. 2018). A long-term decreasing trend in AOD over East Asia beginning in 2011 has been identified (e.g., Kim et al. 2017). Diurnal variations of aerosol properties are captured well by GOCI (Lennartson et al. 2018). Diurnal variations of AODs usually occur with human activity cycles, outbreak of wildfires, and long-range transport. Monthly average AODs show clear diurnal variation (Fig. 2, top panel). Morning peaks in AOD are shown over China and Korea that correspond to one of the dual peaks as reported by Lennartson et al. (2018).
To date, no observations of trace gases at high temporal resolution from GEO have been made to complement the high-temporal-resolution aerosol measurements. Fishman et al. (2008) and Bovensmann et al. (2004) discussed the importance of a GEO mission to capture the diurnal variations of air pollutant concentrations due to photochemistry, time-dependent emissions, and daily meteorological variability. This is illustrated in Fig. 2 bottom panel, which shows the diurnal variations of gas concentrations simulated by CMAQ, together with AOD measured on 25 May 2016. This represents a long-range transport case resulting in high concentration during the joint Korea–United States KORUS-AQ field campaign (Lee et al. 2019; Choi et al. 2019). KORUS-AQ integrated models and measurements from ground-based, airborne, shipborne, and satellite platforms to diagnose AQ problems in Korea [National Institute of Environmental Research (NIER); NIER and NASA 2017]. Such scientific findings along with societal demands have led to projects aimed at providing hourly observations of trace gas column abundance from space. To convert column density to surface concentration, vertical profile information is very important, as discussed later. The Geostationary Environment Monitoring Spectrometer (GEMS) is planned to be launched no later than March 2020, to monitor AQ at high spatial and temporal resolution from GEO over Asia. GEMS is a part of the future GEO AQ constellation, together with the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument covering North America (Zoogman et al. 2017), and the Sentinel-4 instrument covering Europe (Ingmann et al. 2012).
Following the increasing interest in AQ in Asia, the GEMS project was initiated by the NIER of Korea in 2008 to establish space-based measurements of AQ at high temporal and spatial resolution (W. J. Choi et al. 2018). Since then, feasibility studies for user requirements, conceptual designs, and science algorithm sensitivity studies have been collaboratively conducted by the Korea Aerospace Research Institute (KARI), NIER, and GEMS science team.
The primary objective of GEMS is to provide columnar measurements of key AQ components—that is, tropospheric O3, aerosols, and their precursors [NO2, SO2, HCHO, and glyoxal (CHOCHO)]—at high temporal and spatial resolution. NO2 is a precursor of tropospheric O3 and nitrate aerosols, as is SO2 for sulfate aerosols. HCHO and CHOCHO provide information on volatile organic compounds (VOCs), which are precursors of tropospheric O3 and organic aerosols (Marais et al. 2012; Zhu et al. 2014). Measurements of HCHO and CHOCHO reflect emissions of biogenic and anthropogenic VOCs including isoprene, monoterpene, and aromatics (DiGangi et al. 2012; Vrekoussis et al. 2010).
The GEMS instrument was jointly developed by KARI and Ball Aerospace and Technology Corp., Boulder, Colorado. GEMS and GOCI-2 instruments on board the GK-2B satellite will make up half of the GK-2 satellite series. The other satellite in the series is the GK-2A, a dedicated spacecraft for the Advanced Meteorological Imager (AMI) at the same longitude 128.25°E (Choi and Ho 2015). The GK-2A was launched successfully in December 2018, and the GK-2B will be launched in February 2020. Table 1 lists the general specifications of instruments on board the GK-1 and GK-2 satellite series. Synchronous measurements of air pollutants by GEMS, together with the meteorological variables, aerosol properties, and ocean properties from AMI and GOCI-2 will provide important synergistic information for the Asia–Pacific region. These three instruments will contribute to a better scientific understanding of the spatiotemporal distribution of pollutants, emission sources, transboundary pollution, and interactions between meteorology and atmospheric chemistry. The frequent observations of GEMS will also increase the number of trace gas observations and improve the accuracy of AQ forecasts, top-down emission rates, and AQ reanalysis datasets. The key science questions of GEMS are summarized as follows:
What are the temporal and spatial variations of concentrations and emissions of trace gases and aerosols that are important for AQ?
How does regional and intercontinental transport affect local and regional AQ?
How does air pollution drive climate forcing and how does climate change affect AQ?
How does meteorology affect AQ?
How can we improve the accuracy of AQ forecasts using satellite measurements?
How can we quantify the outflow of pollutants from Asia across the Pacific Ocean?
Major specification of instruments on board the GEO-KOMPSAT (GK) series. FD: full disk; NH: Northern Hemisphere.
To achieve the GEMS mission objectives, a set of user requirements were developed after extensive sensitivity studies and data analysis of previous satellite measurements. Nominal spatial resolution is 7 km × 8 km for gases and 3.5 km × 8 km for aerosols over Seoul, South Korea. To detect the trace gases of interest, the spectral coverage of GEMS was chosen to be 300–500 nm at 0.6 nm full-width at half-maximum (FWHM) with 3 samples/band. Typical vertical optical depths (ODs) for relevant trace gases and aerosols in the GEMS spectral range are shown in Fig. 3. Low- and high-frequency signals can be separated to provide ODs for aerosols and trace gases, respectively. Additional details of user requirement analysis can be found in the supplement.
GEMS, as shown in Fig. 4, is a step-and-stare UV–visible imaging spectrometer, with a Schmidt telescope and Öffner spectrometer. A UV-enhanced charge coupled device (CCD) with 2,000 × 1,000 pixels combines signals from 2,000 north (N)–south (S) spatial pixels at a given east (E)–west (W) scan position, and 1,000 spectral channels in the spectral range 300–500 nm.
The field of regard (FOR) of GEMS was chosen to cover most of Asia (5°S–45°N in latitude and 75°–145°E in longitude, as shown in Fig. 5. GEMS scans its E–W coverage in 701 steps over 30 min, and transmits data to ground during the next 30-min imaging time of GOCI-2. While the maximum E–W scan angle is fixed (∼45° in longitude at equator), the scan pattern is flexible within the FOR, with nominal daily, central, and west scan patterns as shown in Fig. 5. For example, with the west scan pattern, AQ over India can be observed in late afternoon after sunset in Seoul. To increase the signal-to-noise ratio (SNR), a reduction of the scan region by half will double the available integration time in the same amount of observing time. In the case of an important event, more frequent scanning over a narrow region, with as short as 15-min resolution, is also possible. GEMS will produce approximately 11 million spectra per day on average.
Daily solar calibrations are performed during Asian nighttime when the sun is at a constant angle behind Earth. Dark calibration is performed before and after daytime measurements. Two diffusers are on board: one for on-orbit daily solar calibration and the other for degradation monitoring every 6 months.
For successful geophysical product retrievals, known as level-2 (L2) data, high-quality spectral radiance measurements, L1b data are critical. Necessary L1 processing steps include corrections for smear, dark current, stray light, and polarization (Fig. ES6 in the supplemental material). Prior to the final assembly of the instrument on the spacecraft, GEMS went through characterization and calibration tests to confirm compliance with the user requirements and to gather sensor data that are critical for on-ground corrections (see the supplement for details). Regular in-orbit spectral calibration and bandpass function characterization will be performed with daily solar irradiance measurements for calibration and trending.
Table 2 lists baseline products and their characteristics for the GEMS. L2 algorithms are based on various methods as listed in the last column of Table 2 with references. To meet the SNR requirements, different spatial and spectral binnings are needed for each product. Unlike existing L2 processors for LEO missions, GEO L2 processors must include the ability to handle diurnal variations in radiative transfer model (RTM) calculations with wider solar zenith angle (SZA) ranges, airmass factors (AMFs), a priori datasets, climatology, stratosphere–troposphere separation, and ancillary datasets including meteorological fields and snow/ice cover. Ongoing improvements consider recent GEMS preflight characterization data. Effective cloud fraction (ECF), cloud radiance fraction (CRF), cloud centroid pressure (CCP), and aerosol effective height (AEH), AOD, aerosol index (AI), and Lambertian equivalent and angle-dependent surface reflectance are commonly used products in all other retrievals (Vasilkov et al. 2017). These algorithms have been tested with L1b data from OMI (Levelt et al. 2018), TROPOMI (Veefkind et al. 2012), airborne GeoTASO during the KORUS-AQ campaign in 2016 (Judd et al. 2018), and simulated radiance spectra using the vector linearized discrete ordinate radiative transfer code (VLIDORT; Spurr 2006) and the Goddard Earth Observing System Chemistry (GEOS-Chem; Bey et al. 2001). All retrievals include vertical column amounts and fitting uncertainty.
Baseline products of GEMS. Nominal spatial resolution is 7 km × 8 km. For products that require spatial binning to meet SNR requirements, spatial resolutions are given in "× n pixel (px)." For example, "× 4 px" means spatial resolution of 14 km × 16 km, "× 2 px" means 14 km × 8 km. For SO2, additional temporal averaging over 3 h are required to compensate weak signals.
GEMS L2 products are to be delivered in two ways: one in near–real time (NRT) for quick operation in reduced quality, and the other for research in better quality through reprocessing with all retrieved products iterated.
Meteorological parameters including temperature profiles, and surface and tropopause pressures can be obtained from the operational Met Office (UKMO) model of the Korea Meteorological Administration (KMA) (Park et al. 2017). The Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011) data were also used for a priori and climatologies.
AMFs are a key parameter to convert slant column density (SCD) to vertical column density (VCD). To meet time constraints for NRT data delivery, AMFs for NO2, SO2, HCHO, and CHOCHO are based on a lookup table (LUT) approach. LUTs are constructed as a function of time, location, observation geometry, surface albedo, etc. Depending on species, spatial and temporal resolutions, cloud and aerosol properties, and vertical profiles are considered differently in the AMF LUTs. Scattering weights are calculated using VLIDORT, and vertical shape factors are constructed as a function of location and time. Details can be found in the subsections below. Ancillary data including snow and ice cover are available from AMI retrievals and the National Snow and Ice Data Center (NSIDC; http://nsidc.org). Averaging kernels and quality flags will be provided as well.
To retrieve total ozone amount (TO3), the TOMS, version 9, algorithm is adopted (Haffner et al. 2015). This algorithm includes improvements to TOMS, version 8.5, previously applied to OMI. For the O3 retrieval, GEMS uses reflectivity at 317.5 nm, derived by linear extrapolation of reflectivity at 340 and 380 nm. The optimal estimation (OE) method is then used at two wavelengths, 312.35 and 331.06 nm, to correct for the ozone profile error. The use of OE is the main difference from the TOMS, version 8.5, which retrieves TO3 using a wavelength pair at 317.5 and 331.2 nm and reflectivity obtained at 331 nm (McPeters et al. 2015). Ozone profile information is determined from ozone spectral line fitting in the window 300–340 nm (Rodgers 2000; Liu et al. 2010; Bak et al. 2013). The absorption cross sections are from the Brion–Daumont–Malicet (BDM; Daumont et al. 1992; Brion et al. 1993; Malicet et al. 1995).
The NO2 algorithm is based on the differential optical absorption spectroscopy (DOAS) technique fitting spectral optical depths of NO2 in the 432–450 nm window to obtain SCDs (Hong et al. 2017a). Troposphere–stratosphere separation for NO2 is performed every hour following the work of Bucsela et al. (2013) and Geddes et al. (2018). SCDs are converted to VCDs using hourly AMFs (Palmer et al. 2001). For AMF calculations, hourly NO2 profile shape factors are obtained from a chemical transport model (CTM), Weather Research and Forecasting Chemistry Model (WRF-Chem; Grell et al. 2011) with the 28 km × 28 km horizontal resolution for the GEMS domain. For pollution hot spots such as Seoul and Beijing, the NO2 concentrations measured at the ground networks will be utilized to enhance the accuracy and spatial resolution of the a priori profile. Earlier work has shown that an explicit aerosol representation in the retrieval algorithm greatly improves the quality of retrieved NO2 data (Lin et al. 2014, 2015; Liu et al. 2019). Hong et al. (2017b) investigated the impact of aerosols on the NO2 AMF and found that aerosol-layer height is particularly important, with some contribution from aerosol single scattering albedo (SSA) and AOD. Additionally, a new attempt has been made to obtain NO2 profiles for high concentration regions using multiple fitting windows across the UV–visible spectrum (Hong 2018). This allows retrieval of NO2 profiles over polluted regions.
The SO2 algorithm fits the spectrum over the 310–326 nm (310–340 nm in volcanic regions) window for planetary boundary layer (PBL) SO2 SCD retrievals using a hybrid algorithm based on DOAS and PCA (Li et al. 2013). The PCA algorithm can reduce noise and bias by using a set of PCs extracted directly from satellite radiance data for "clean sectors" that are free of SO2 emissions. However, in cases where a clean sector is contaminated with high AOD, O3, or SO2, large uncertainties occur in the SO2 SCD retrieval (Yang et al. 2018). The hybrid algorithm can reduce these uncertainties by using the DOAS method to filter the SO2-contaminated pixels and determine clean sectors, particularly when high concentrations of these interfering pollutants are unintentionally included in a clean sector or there are not enough clean sector pixels in the GEMS domain. The hybrid algorithm first uses the DOAS technique to determine clean sector for extracting principle components (PCs). Then these PCs are fitted to measured radiances with the cross sections to retrieve SO2 SCDs using the PCA method. For AMF calculations, box profile shape with top altitude at 1 km is assumed for PBL SO2, while similar box profiles with top at up to 15 km are for volcanic SO2 based on NASA climatology.
The HCHO and CHOCHO retrieval algorithms are based on a nonlinear fitting method, referred to as direct fitting (DF), in the fitting windows of 328.5–356.5 nm and 435–461 nm, respectively (Chance et al. 2000; González Abad et al. 2016, 2015; Chan Miller et al. 2014; Kwon et al. 2017, 2019). For AMF calculations, vertical profile shapes are from the GEOS-Chem driven by MERRA. Kwon et al. (2017) showed that the effect of aerosols on AMF was nonnegligible in retrievals of HCHO columns. This is particularly true for East Asia, where aerosol and other trace gas concentrations vary considerably in time and space. Hourly AMF calculations are planned to use a fast LUT approach with the GEMS aerosol products. Finally, a systematic bias correction is performed for each pixel, but it can be avoided by using measured radiances over a clean background region as a reference spectrum for radiance fitting.
The aerosol algorithm is based on OE and the OMI aerosol algorithm to retrieve AOD, SSA, and AEH (Torres et al. 2013; Park et al. 2016; Jeong et al. 2016; Kim et al. 2018). Following aerosol type classification using UV and visible AIs, AOD and SSA are retrieved using measurements at 354 and 388 nm (Herman et al. 1997). Using the retrieved AOD and SSA as a priori, AEH is retrieved by OE using six selected wavelengths, including the O2–O2 absorption band at 477 nm. To detect absorbing aerosols, the UV AI, the absorbing AOD (AAOD) and the absorption Ångström exponent (AAE) will be provided.
Merging data from GEMS with those from AMI and/or GOCI-2 can significantly improve the accuracy of aerosol products by adopting the cloud mask from the IR channels of AMI, combining L2 aerosol products from GOCI-2 and AMI, and combining L1b data from all three instruments. AMI and GOCI-2 provide aerosol properties at higher resolution mostly over darker surfaces only. However, GEMS can provide aerosol properties such as AOD and radiative absorptivity even over bright land surfaces. As the time difference between AMI and GEMS measurements is <10 min, synergy between the two instruments is readily achieved considering the typical lifetime of aerosols and clouds in the atmosphere.
Cloud information is critical to most of the GEMS products as the accuracy and retrievability of trace gas products is subject to cloud effects (Ahmad et al. 2004; Antón and Loyola 2011; Bucsela et al. 2013; van Diedenhoven et al. 2008). GEMS cloud products (ECF and CCP) are based on the Lambertian cloud model (Stammes et al. 2008). The DOAS algorithm for the O2–O2 absorption line at 477 nm is used to retrieve the ECF and CCP from the O2–O2 column amounts above clouds (Acarreta et al. 2004; Stammes et al. 2008; Veefkind et al. 2016). ECF can also be converted to spectral CRF, the ratio of cloud radiance to total observed radiance, for use in the different spectral fitting windows of the trace gases.
Although surface reflectance is an important factor in retrievals of the atmospheric composition, many previous studies have assumed isotropic surface reflectance (e.g., Kim et al. 2007; Bak et al. 2013; Kwon et al. 2017). One advantage of GEMS over LEO instruments lies in its ability to derive directional reflectance using high-frequency observations over short periods (e.g., a day). Two kinds of surface reflectance products are retrieved from GEMS: the geometry-dependent Lambertian equivalent reflectivity (GLER) and the daily bidirectional reflectance distribution function (BRDF; Lee and Yoo 2018). The GLER algorithm will compile clear-sky composite reflectance from GEMS, and the BRDF model will compile GEMS measurements for various illumination-viewing geometries at each pixel. The assumption of Lambertian reflection can lead to biases in GLER retrievals using OMI L1b data (Vasilkov et al. 2017). Retrieved GLER results using OMI L1b data lead to relative errors of 0.024 and a RMSE of 0.029, compared to MODIS L2 products. The uncertainty approaches 20% for SZA and viewing zenith angle (VZA) > 60° and for turbid conditions. The parameters of the kernel-driven BRDF model are derived by statistical fitting (Lucht et al. 2000; Roujean et al. 1992).
The UV index (UVI) algorithm is based on radiative transfer modeling using inputs of the retrieved TO3, cloud optical depth (COD) estimated from the reflectance at 354 nm, and climatological surface albedo data. The UVI and three different indexes are obtained by applying different action spectra to the UV radiance spectrum obtained: erythemal (McKinlay and Diffey 1987), DNA damage (Setlow 1974), vitamin D production in human skin (CIE 2006), and a plant response (Caldwell 1971). Aerosol correction is also applied following the OMI and TROPOMI correction method (Arola et al. 2009; Lindfors et al. 2018). For the surface, OMI LER climatology (Kleipool et al. 2008) is used until GEMS LER becomes available.
The performance of the GEMS L2 algorithm was tested using one year of OMI L1b data for 2005 as a proxy dataset prior to the launch of GEMS, as shown in Fig. 6 and Table 3. Most of the GEMS retrievals are well correlated with the ground-based and/or operational products of OMI and MODIS, with most of the data points falling near the 1:1 line. Figure 6a shows retrieved tropospheric O3 compared with ozonesonde observations at Pohang, Hong Kong, Sepang, and Isfahan (Fig. 6a). Tropospheric O3 shows high concentration in East Asia and northeast India in particular, and low near the equator and Tibetan Plateau. Total ozone shows excellent agreement as shown in Fig. 6b. Total NO2 VCD in Fig. 6c are higher over mega cities, where systematic overestimation is due to the use of different spectroscopy, slit function characterizations and a reference radiance spectrum for fitting procedures. The SO2 comparisons in Fig. 6d shows large values over eastern China and reflect Anatahan volcano in April 2005. Agreements are reasonable despite of weak signals and different algorithms. Further validations are needed with surface SO2 measurements. HCHO in Fig. 6e shows polluted region in Asia, especially in Indochina and Indonesia. HCHO comparison shows good agreements despite different algorithms, where negative values are shown on purpose to indicate uncertainties for both GEMS and OMI. AOD was compared with AERONET within the FOR and shows reasonable results considering the coarse spatial resolution of OMI, as shown in Fig. 6e. The scattered data points for UVI in Fig. 6g are primarily caused by the effect of LUT-based cloud corrections. The surface reflectance comparison in Fig. 6h is for LER and is expected to improve with results of ongoing work on angle dependencies. ECF and CCP are in good agreement with OMI operational products, as shown in Figs. 6i and 6j.
Intercomparison results of GEMS algorithm using OMI L1b data V003.
For GEMS mission success in maintaining accurate and consistent L2 products, a well-defined validation strategy has been developed based on comparison of retrieved products with in situ ground measurements. A summary of network sites within GEMS domain are shown in Fig. 7. GEMS L2 products can be validated with column measurements from Pandora instruments (Herman et al. 2009), AERONET (Holben et al. 1998), and the Sun–Sky Radiometer Observation Network (SONET) (Li et al. 2018). Vertical profiles can be obtained from the Korean Aerosol Lidar Observation Network (KALION; Yeo et al. 2016), the Asian Dust and aerosol lidar observation Network (AD-Net) (Shimizu et al. 2016), and the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) networks (Kanaya et al. 2014). Collocated in situ surface concentration measurements are essential to investigate relationship between column measurements and surface concentrations. Surface concentrations of trace gases and aerosols are available from the more than 400 ground stations of AirKorea including six supersites (www.airkorea.or.kr), acid deposition Network in East Asia (EANET; Sugimoto and Uno 2009), and the WMO Ozone and UV Data Center (WOUDC; https://woudc.org/). Details of each measurement network can be found in Table ES4. Excellent opportunities to validate the GEMS products will also be afforded by intensive aircraft field campaigns such as the future KORUS-AQ 2 and Megacity Air Pollution Studies (MAPS)-Seoul 2 that are being planned for 2021–23.
With the high-spatial- and high-temporal-resolution measurements of GEMS, significant improvements are expected in top-down emission rate (TDE) estimates and data assimilation (DA). The TDEs and DA with satellite data can be dramatically improved moving from daily resolution (LEO) to those with hourly resolution (GEO). High-spatial-resolution data can also help resolve sub-grid-scale features of CTM simulations. GEMS data will be served to the public in terms of surface concentrations, AQ forecasts, and warnings.
Bottom-up emission rates (BUEs) are critical for accurate AQ forecasts by CTMs. BUE inventories including the Clean Air Policy Support System (CAPSS), the Regional Emission inventory in Asia (REAS), and the Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment (CREATE), the Multi-Resolution Emission Inventory for China (MEIC) have improved significantly to date (Lee et al. 2011; Kurokawa et al. 2013; Ohara et al. 2007; Woo et al. 2013; Li et al. 2017). Recent BUEs have more comprehensive list of fuels, sectors, pollutants with better estimation parameters, such as newer energy statistics, local emission factors, finer spatial surrogates, and so on. However, the uncertainties of BUE data are still relatively high because they are typically estimated based on the statistical summaries of human activities, a limited number of emission factors, and the application of control measures (Li et al. 2017). In addition, the long lead time of annual BUE compilation delays timely updates with year-to-year trends and daily human activities.
TDEs will clearly benefit from the more frequent observations of GEMS. With TDE estimates, an evaluation of AQ using inverse modeling can be performed to improve existing emission information in a timely manner with better accuracy (Mijling et al. 2013). Long-term satellite measurements can also contribute to emission rates by detecting missing emission sources and hot spots (Fioletov et al. 2011; McLinden et al. 2016). Increasingly stringent AQ improvement policies in China, Japan, and Korea have led to rapidly changing BUE estimates of source magnitude and spatial distributions in these regions. The long-term, high spatiotemporal resolution of GEMS observations will enable monitoring of AQ changes over northeast Asia due to the adoption of national emission control policies (Duncan et al. 2016; Souri et al. 2017).
The GEMS-retrieved products will be utilized by the Korean Air Quality Forecast System (KAQFS) being developed under the framework of the Korean National Strategic PM Project launched in 2017. To enhance the accuracy of short-term (24–48 h) AQ forecasts over South Korea, accurate initial conditions (ICs) are of critical importance. The KAQFS currently uses the GOCI AODs with ground-based PM measurements for aerosols, which has demonstrated good prediction performance for PM10 and PM2.5 over South Korea (Lee 2018). Ongoing work seeks to further enhance forecasting performances for gases including O3 and precursors, by improving accuracy of ICs and better constraining emissions using hourly data. To accomplish the preparation of the ICs and improve their accuracy, GEMS hourly data will be used together with the surface concentration measurements from ground-based observation networks in Asia.
The importance of multisatellite data assimilation was demonstrated by the successful optimization of initial concentration and emission fields using the KORUS-AQ campaign dataset (Miyazaki et al. 2019). The value of a GEO satellite dataset for DA has been demonstrated for NO2 and O3 (Liu et al. 2017; Zoogman et al. 2011) with observing system simulation experiments (OSSEs), and for aerosols with GOCI (Park et al. 2014; Saide et al. 2014). Key technical components of these systems are the development of the DA techniques and adequate observation operators. Currently, DA techniques based on optimal interpolation with Kalman filters, the three-dimensional variational data assimilation (3D VAR) methods, and ensemble Kalman filters (EnKFs) have been developed and applied to the KAQFS.
An ensemble-based meteorology–chemistry coupled DA system has been developed by interfacing WRF-Chem with the maximum likelihood ensemble filter (MLEF; Županski 2005). Park et al. (2015) showed that, in the coupled DA system, the cross-variable components of forecast error covariance made physically meaningful adjustments to all the control variables. They also showed that the coupled error covariance allowed cross-component impacts (e.g., Lim et al. 2015; Lee et al. 2017). These studies have demonstrated the benefits of using an ensemble-based coupled meteorology–chemistry DA to improve the analysis fields of both meteorological and chemical variables. It is expected that the aerosol and trace gas observations from GEMS will further improve the performance of coupled meteorology–chemistry models in forecasting both weather and AQ by providing tighter observational constraints on the DA through the high density of GEMS measurements. The GEMS observations are anticipated to be utilized in many applications including AQ forecasts, AQ reanalysis systems, radiative forcing estimation, and so on (e.g., Marécal et al. 2015; Lee et al. 2016; Benedetti et al. 2009; Inness et al. 2013; IPCC 2013; Lee et al. 2014).
GEMS will provide a revolutionary public service by improving AQ forecasts, enabling early warning systems, and providing AQ data to all regions, both urban and rural. Hourly GEMS L2 datasets will be distributed to the public through multiple platforms including smartphones. For public use, AOD data from satellite measurements can be converted to surface PM2.5 and PM10 using CTM results, statistical analysis, and machine learning (e.g., Xu et al. 2015; Seo et al. 2015; Park et al. 2019). A similar algorithm under development at NIER will be applied to trace gas measurements. GEMS can also provide AQ data for countries such as North Korea where measurements have not been available to date, or developing countries where measurements are sparse. In combination with existing datasets, GEMS data can be utilized to fill the data gap in space, and assess the impact of PM on public health, traffic on urban pollution, power plants on regional pollution, ship emissions on marine pollution, and O3 on crop yields. It is anticipated that GEMS data will also lead to a better environmentally informed society and contribute to environmental policy and international treaties.
Sometime in the early 2020s, there will be a series of GEO AQ satellites, the so-called GEO AQ constellation, with GEMS over Asia beginning in 2020, followed by NASA TEMPO over North America, and ESA Sentinel-4 over Europe. These missions offer similar observational capabilities, similar level-2 product portfolios, and are committed to serving the data needs of AQ applications. These missions cover the major industrialized regions of the Northern Hemisphere (Fig. 8). The GEO AQ missions will be complemented by several LEO missions listed above and potentially future missions. LEO missions fill data gap over regions not covered by the GEO AQ missions and serve as traveling standards for assessing and improving mutual consistency between the products of the geostationary missions.
GEMS is scheduled for launch in February 2020, to monitor AQ at unprecedented spatial and temporal resolution from GEO over Asia. L2 science algorithms for the various products are based on up-to-date techniques, and tested with existing satellite L1b data. The predicted performance of GEMS suggests that most of the user requirements will be met. Continued efforts to upgrade both the L1 and L2 processors using updated information will ensure the success of the mission. Well-coordinated ground-based networks are essential components in the validation of the GEO AQ missions.
Chemical weather forecasting is about to experience a revolution that meteorologists have had ever since the launch of the first geostationary weather satellites. GEMS data at high temporal and spatial resolution can be used widely in improving AQ forecasts and emission rates, and providing AQ information services to public. The three GEO AQ missions together with synergistic meteorological measurements, will improve our understanding of transport, and chemical and physical processes by integrating multiplatform, cross-scale observational assets. The GEO AQ constellation will be the first of its kind to monitor global AQ in a coordinated manner with LEO satellites and to implement scientific policy using high-resolution space-based measurements.
The GEMS program is supported by the National Institute of Environmental Research (NIER), the Ministry of Environment, South Korea. This project is supported by the Korea Ministry of Environment (MOE) as Public Technology Program based on Environmental Policy (2017000160001). Research at the Smithsonian Astrophysical Observatory was supported by a NASA USPI Grant "SAO Participation in the Korean Geostationary Environment Monitoring Spectrometer (GEMS): Instrument Design and Algorithm Development." Part of the research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Work done by HI was supported by the Environment Research and Technology Development Fund (2-1901) of the Environmental Restoration and Conservation Agency of Japan, JSPS KAKENHI (JP19H04235 and JP17K00529), the JAXA 2nd research announcement on the Earth Observations (19RT000351), and JST CREST (JPMJCR15K4).
Appendix: List of acronyms
3DVAR
Three-dimensional variational data assimilation
Multi-Viewing Multi-Channel Multi-Polarization Imager
Absorption Ångström exponent
Absorbing aerosol index
AAOD
Absorbing aerosol optical depth
Advanced Baseline Imager
AC-VC
Atmospheric Composition Virtual Constellation
AD-NET
Asian Dust and aerosol lidar observation Network
AEH
Aerosol effective height
AERONET
Aerosol Robotic Network
Advanced Geostationary Radiation Imager
Advanced Himawari Imager
Aerosol index
Atmospheric Infrared Sounder
Averaging kernel
Airmass factor
Advanced Meteorological Imager
Aerosol optical depth
Brion–Daumont–Malicet
Bidirectional reflectance distribution function
Bromine monoxide
BTDF
Bidirectional transmittance distribution function
Bottom-up emission rate
Glyoxal
CAPSS
Clean Air Policy Support System
Charge coupled device
Cloud centroid pressure
Committee on Earth Observation Satellites
Commission Internationale de l'Eclairage
CMAQ
Community Multiscale Air Quality
Cloud optical depth
Communication, Oceanography and Meteorology Satellite
Comprehensive Regional Emissions inventory for Atmospheric Transport Experiment
Cloud radiance fraction
Cross-track Infrared Sounder
Chemical transport model
Direct fitting
Deoxyribonucleic acid
DOAS
Differential optical absorption spectroscopy
Dobson unit
EANET
Acid deposition monitoring Network in East Asia
Effective cloud fraction
Environment Monitoring Instrument
EnKF
Ensemble Kalman filter
Earth Polychromatic Imaging Camera
Environmental Satellite Center
European Organisation for the Exploitation of Meteorological Satellites
Flexible Combined Imager
Field of regard
FPE
Focal plane electronic
FWHM
Full-width at half-maximum
FY-4
Fengyun-4
Global Atmosphere Watch
GAW WDCA
Global Atmosphere Watch World Data Centre for Aerosols
GCOM
Global Change Observation Mission
Geostationary Environment Monitoring Spectrometer
Geostationary Earth orbit
GEO-KOMPSAT
Geostationary Korea Multi-Purpose Satellite
Goddard Earth Observing System Model, version 5
GEOS-Chem
Goddard Earth Observing System Chemistry
GeoTASO
Geostationary Trace gas and Aerosol Sensor Optimization
GaoFen-5
GK-2
GEO-KOMPSAT-2
GK-2A
GEO-KOMPSAT-2A
GK-2B
GEO-KOMPSAT-2B
GLER
Geometry-dependent Lambertian equivalent reflectivity
Geostationary Ocean Color Imager
GOCI-2
Geostationary Ocean Color Imager 2
GOES-8
Geostationary Operational Environmental Satellite 8
GOES-R
Geostationary Operational Environmental Satellites R series
GOES-S
Geostationary Operational Environmental Satellites S series
Global Ozone Monitoring Experiment
Global Atmosphere Watch Urban Research Meteorology and Environment
H2CO
Water vapor
HCHO
HONO
Nitrous acid
Infrared Atmospheric Sounding Interferometer
Initial condition
INSAT-3D
Indian National Satellite 3D
Image navigation and registration
Iodine monoxide
KALION
Korea Aerosol Lidar Observation Network
KAQFS
Korean Air Quality Forecasting System
Korea Aerospace Research Institute
Korea Meteorological Administration
KORUS-AQ
Korea–United States Air Quality study
Korea standard time
L1b
Level 1b
Low Earth orbit
Lambertian equivalent reflectivity
Long-range transboundary air pollutants
Lookup table
Multi-Angle Imager for Aerosols
MAPS-Seoul
Megacity Air Pollution Studies—Seoul
MAX-DOAS
Multi-Axis Differential Optical Absorption Spectroscopy
Multi-Resolution Emission Inventory for China
Meteorological Imager
Multi-Angle Imaging Spectroradiometer
MLEF
Maximum likelihood ensemble filter
Moderate Resolution Imaging Spectroradiometer
Measurements of Pollution in the Troposphere
MPLNET
Micro-Pulse lidar Network
Modulation Transfer Function
MTG-I
Meteosat Third Generation—Imaging
MTG-S
Meteosat Third Generation—Sounding
Multifunctional Transport Satellite
National Institute of Environmental Research
NIES
National Institute for Environmental Studies
Near-infrared
Nitric oxide + nitrogen dioxide
O2–O2
Collision-induced oxygen complex
Optical depth
Optimal estimation
Ozone Monitoring Instrument
OMPS
Ozone Mapping Profiler Suite
Observing System Experiment
Observing System Simulation Experiment
Principal component
Principal component analysis
Particulate matter (diameter < 10 µm)
Particulate matter (diameter < 2.5 µm)
PRNU
Pixel response nonuniformity
Quality flag
Relative azimuth angle
Regional Emission inventory in Asia
RMSE
Root-mean-square error
Radiative transfer model
Sentinel-5 Precursor
SBUV
Solar Backscatter Ultraviolet radiometer
Slant column density
Scanning Imaging Absorption Spectrometer for Atmospheric Cartography
Sea-Viewing Wide Field-of-View Sensor
Spinning Enhanced Visible and Infrared Imager
SGLI
Second-Generation Global Imager
Skyradiometer Network
Sun–Sky Radiometer Observation Network
Single scattering albedo
Std dev
Solar zenith angle
TDE
Top-down emission rate
Tropospheric Emissions: Monitoring of Pollution
Tropospheric Emission Spectrometer
Total ozone amount
Total Ozone Mapping Spectrometer
Tropospheric Monitoring Instrument
Coordinated universal time
UVAI
Ultraviolet aerosol index
Ultraviolet index
Ultraviolet, visible, and near-infrared
Vertical column density
Visible Infrared Imaging Radiometer Suite
VLIDORT
Vector linearized discrete ordinate radiative transfer
Volatile organic compound
VZA
Viewing zenith angle
WOUDC
WMO Ozone and UV Data Center
WRF-Chem
Weather Research and Forecasting Chemistry Model
YAER
Yonsei Aerosol Retrieval
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