To meet the growing demand for diurnal information on trace gases and aerosols in the atmosphere, a series of satellite programs consisting of the GEO-ring (GEO-constellation) has been initiated. The series started off with the launch of the Geostationary Korean Multi-Purpose Satellite-2B (GK-2B) in 2020, followed by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) in 2023 and the expected launch of Sentinel-4 in 2024. Onboard GK-2B, the Geostationary Environment Monitoring Spectrometer (GEMS) is dedicated to observing the Asia-Pacific region, providing spectral radiance in the 300-500 nm range to obtain specific spectral information on absorption and scattering lines. To evaluate the post-launch data quality of GEMS, especially for Level 1B products, this study utilizes inter-calibration approaches with the measurements from geostationary as well as polar orbit satellite sensors. The evaluation comprises two parts to address current and potential calibration issues of GEMS: 1) applying the ray-matching approach with the Advanced Meteorological Imager (AMI) onboard the twin satellite, GK-2A; and 2) employing vicarious calibration with polar orbit satellite sensors, Tropospheric Monitoring Instrument (TROPOMI) and Ozone Mapping and Profiler Suite (OMPS), targeting stable scenes on Earth. In the first approach, AMI and GEMS demonstrate a strong agreement, showing a high correlation coefficient exceeding 0.9 regardless of measurement time and season. However, the GEMS Level 1B product reveals a positive bias when compared to AMI, 10% and 5% for radiance and reflectance, respectively. The GEMS measurements also display distinct seasonal and diurnal variations compared to AMI, which needs further investigation considering that the variations could influence the Level 2 retrieval products of GEMS. In the second approach, GEMS shows residual stray light effect especially at the shorter wavelengths (below 320 nm) and quantitatively, GEMS shows a consistent bias with the first approach, when compared to TROPOMI and OMPS. The paper aims to provide valuable insights for the efficient monitoring of sensors under comparable conditions with GEMS, along with remaining challenges emphasizing the need for refined approaches to address the radiometric calibration accuracy of the GEMS Level 1B product.

Geostationary Environmental Monitoring Spectrometer (GEMS), on-board Geostationary Korea Multi-Purpose Satellite-2B (GK-2B), is the first geostationary environmental instrument to be the Asian component of the global geostationary constellation for pollution monitoring together with the European Sentinel-4 and the North American Tropospheric Emissions: Monitoring of Pollution (TEMPO). GEMS is a hyper grating spectrometer that hourly measure backscattered solar spectral radiance from the ultraviolet to visible (300 to 500 nm) with 0.6 nm spectral resolution and 3.5 (7 for trace gases) * 8 km2 spatial resolution over the Seoul in the daytime. These radiances are used to retrieve spatial and temporal distributions of aerosol and trace gases such as O3, NO2, SO2 and HCHO. Since the performance of trace gas retrieval strongly depends on the quality of raw data, the in-flight characteristics have been analyzed and calibrated in detector, radiometric and spectral aspects. Furthermore long-term performance of GEMS have been monitored and analyzed for the dark current, electronic offsets, non-linearity, diffuser and the output of the internal light sources. The orbital performance of GEMS shows that the number of incurred dead and bad pixels due to cosmic-ray impacts is 0.05% increased over the three full year since the operations. The dark signal distributions of on-orbit dark images show 9.02% increased dark mean which indicates a gradually degraded detector CCD performance. The orbital electronic biases from averaging trailing pixels show quite stable status in orbit around 900-1050 counts depending on each quadrant. In-orbit PRNU (< 7%) and non-linearity(< 2%) of 5-95% CCD full well signals meet the system requirements and show stable status. However, there is a possibility that remaining 10% non-linearity effects on the shortwave signals under the 5% CCD full well in early morning or late afternoon. Trends over the three full year of nominal operations indicates stable status of GEMS with gradually degraded detector and diffuser performances. The detailed results and the orbital and long-term stability for internal light source (LED), dark currents and solar irradiance measurements are going to be presented.

Abstract To address the increasing demand for diurnal information on trace gases and aerosols, a series of geostationary (GEO) satellite programs called GEO‐constellation have been initiated, with the launch of the Geostationary Environment Monitoring Spectrometer (GEMS) onboard Geostationary Korea Multi‐Purpose Satellite 2B (GK2B). To assess the sensor performance of GEMS in orbit, the current work suggests employing an inter‐calibration methodology involving the Advanced Meteorological Imager (AMI) aboard its twin satellite, GK2A. Twin satellites have a significant advantage in obtaining collocation data sets across diverse spatiotemporal, angular, and atmospheric conditions, enabling rigorous collocation criteria effectively reducing mismatch uncertainty. The results present robust correlation coefficients over 0.99, revealing the current calibration characteristics of the sensors. This research emphasizes the advantages of the GEO‐GEO inter‐calibration, particularly the capability of analyzing spatial and temporal dependencies. These findings confirm the mutual benefit of utilizing the sensors in similar configurations, highlighting their importance for future satellite monitoring endeavors.

Geostationary Environment Monitoring Spectrometer (GEMS), the first UV-Vis hyperspectral imaging spectrometer onboard a geostationary satellite launched in February 2020, is working with overall performances as well as characteristics aligned with ground-based characterizations. However, there are noticeable issues, especially in the solar irradiances which show a significant discrepancy compared to reference datasets, the focus of current study. The key discrepancy is the variation of measured solar irradiance along the time as well as space of which the root causes are traced back to the angular dependence of the diffuser transmittance and its degradation, both of which critically impact the accuracy of the GEMS Level-2 data products. To mitigate the discrepancy, the current study introduces an empirical correction approach that uses the correlation between the azimuth angle and the measured daily irradiance using 3.5 years of data. With the correction, the spatial and seasonal discrepancies in both irradiance and Earth reflectance disappeared almost completely. Furthermore, the mean bias and root-mean-square deviation (RMSD) against the solar reference spectrum decreased by 12% and 5%, respectively. However, the corrected irradiance values are still lower than those from reference data and other satellites, indicating the potential need for future updates to the radiometric calibration coefficients.

Abstract. Earth radiance in the form of hyperspectral data contains useful information on atmospheric constituents and aerosol properties. The Geostationary Environment Monitoring Spectrometer (GEMS) is an environmental sensor measuring such hyperspectral data in the ultraviolet and visible (UV/VIS) spectral range over the Asia-Pacific region. After successful completion of the in orbit test of GEMS in October 2020, bad pixels are found as a remaining calibration issue to be updated with follow-up treatment. Currently, one-dimensional interpolation in the spatial direction is performed in operation to replace the erroneous pixels of GEMS, which causes high interpolation error for a wider defect area on a detector array. To resolve the issue, this study suggests machine learning methods with artificial neural network (ANN) and multivariate linear regression (Linear) to fill in a spectral gap of defective spectra. The machine learning models are trained with normal measurements to emulate spectral relations between input and output radiances in a spectrum. For efficient training, dimensionality reduction for the input radiances is applied with principal component analysis (PCA) prior to the training process. The results show that the defect area at the wavelengths of strong absorption lines is better replaced with PCA-ANN with the error of 5 %, while PCA-Linear is better for reproducing radiances having strong correlation with input radiances. The shorter the spectral range of output radiances is, the smaller the prediction error is with PCA-Linear (0.5–5 %). Spectral and spatial discontinuity caused by real bad pixels can be significantly improved with the trained machine learning models especially for wide defect areas. This study verifies that spectral relations of radiances in the UV/VIS spectrum are successfully reproduced with a simple machine learning model, which has high potential to be investigated further for enhancing measurement quality of environmental satellite measurements.

The Geostationary Environment Monitoring Spectrometer (GEMS), an ultraviolet and visible imaging spectrometer, provides air-quality information over a large area of the Asia Pacific region with a high spatiotemporal resolution. To assure the reliability of trace gas retrieval, accurate knowledge of the spectral response function (SRF) is critical for spectral calibration as well as retrieval algorithms. Here, we characterize the GEMS SRF using prelaunch SRFs obtained with the monochromatic laser measurements during the ground test and inflight SRFs retrieved using the solar irradiance measurements after the launch. The prelaunch SRFs are analyzed in terms of shape (skewness and kurtosis), width, and under-sampling and show that the full-width at half-maximum is smaller than 0.6 nm with a maximum of 0.589 nm. The variations along both the spectral and spatial directions are smooth and within 3.65%, indicating a highly homogenous and stable optical system of GEMS. To characterize the prelaunch SRFs and monitor the behavior of inflight SRFs, we applied several analytical functions including asymmetric super Gaussian (ASG) and hybrid Gaussians to the prelaunch SRFs. The spectral fitting of the measured GEMS irradiance with a reference spectrum shows that the ASG to be the best representative of the GEMS SRFs. The inflight SRFs, retrieved with the GEMS irradiances and the ASG, agree well with the prelaunch SRFs, suggesting that the inflight spectral performance and characteristics of GEMS are similar to those investigated from the on-ground characterization.

The Geostationary Korean Multi-Purpose Satellite (GK-2) program consisting of GK-2A and GK-2B provides consistent monitoring information in the Asia Pacific region, including the Korean peninsula. The Geostationary Environment Monitoring Spectrometer (GEMS) onboard GK-2B in particular provides information on the atmospheric composition and aerosol properties, retrieved from the calibrated radiance (Level 1B) with high spectral resolution in 300-500 nm. GEMS started its extended validation measurement after the in-orbit test (IOT) in October following the launch of the satellite in February 2020. One of issues found during the IOT is that GEMS shows a spatial dependence in the measured solar irradiance along the north-south direction, albeit the solar irradiance does not have such a dependency. Thus, such a dependence should be from the optical system or the solar diffuser which is placed in front of the scan mirror. To clarify the root cause of the dependence, we utilize inter-comparison of the Earth measurement between GEMS and the Advanced Meteorological Imager (AMI), a multi-channel imager onboard GK-2A for meteorological monitoring. As the spectral range of GEMS fully covers the spectral response function (SRF) of the AMI visible channel having a central wavelength of 470 nm, spectral matching is properly done by convolving the SRF with the hyperspectral data of GEMS. By taking advantage of the fact that the position of GK-2A and GK-2B is maintained within a 0.5 degree square box centered at 128.2°E, match-up data set for the inter-comparison is prepared by temporal and spatial collocation. To reduce spatio-temporal mis-match and increase the signal to noise, zonal mean is applied to the collocated data. Results show that the north-south dependence occurs in the comparison of reflectance, the ratio between the earth radiance and solar irradiance, while not in the comparison of radiance. This indicates the dependence occurs due to the characteristics of the solar diffuser, not because of optical system. It is further deduced that dependence of diffuser transmittance on the solar azimuth angle is the main cause of the north-south dependency which was not characterized during the pre-flight ground test.

The successful launch of Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korea Multipurpose Satellite 2B (GK-2B) opens up a new possibility to provide daily air quality information for trace gases and aerosols over East Asia with high spatiotemporal resolution. As a part of major efforts to calibrate and validate the performance of the GEMS, accurate characterization of the spectral response functions (SRFs) is critical. The characteristics of preflight SRFs examined in terms of shape, width, skewness, and kurtosis vary smoothly along both the spectral and spatial direction thanks to highly symmetrical optic system of GEMS. While the preflight SRFs are determined with high accuracy, there is possibility of changes of in-flight SRFs during the harsh launch processes and/or operations over the mission lifetime. Thus, it is important to verify the in-flight SRFs after launch and to continue monitoring of their variability over time to assure the reliable trace gases retrievals. Here, we retrieve the in-flight SRFs for all spectral and spatial domain of the GEMS using spectral fitting of observed daily solar measurement and high-resolution solar reference spectrum. A variety of analytic model functions including hybrid form of Gaussian and flat-topped function, asymmetric super Gaussian, Voigt function are tested to determine the best representative function for GEMS SRF. The SRFs retrieved from early solar irradiances measured during the in-orbit tests agree well with the preflight SRFs indicating that no significant change occurred during the launch process. Continuous monitoring of the in-flight SRF is planned, using daily solar irradiances to investigate the temporal variation along with spectral and spatial directions. The detailed results of the in-flight SRF retrieval are to be presented.

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).