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.

Earth radiances in the form of hyperspectral measurements contain 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 spectral range over the Asia–Pacific region. After completion of the in-orbit test of GEMS in October 2020, bad pixels are found as one of remaining calibration issues resulting in obvious spatial gaps in the measured radiances as well as retrieved properties. To solve the fundamental cause of the issue, this study takes an approach reproducing the defective spectra with machine learning models using artificial neural network (ANN) and multivariate linear regression (Linear). Here the models are trained with defect-free measurements of GEMS after dimensionality reduction with principal component analysis (PCA). Results show that the PCA-Linear model has small reproduction errors for a narrower spectral gap and is less vulnerable to outliers with an error of 0.5 %–5 %. On the other hand, the PCA-ANN model shows better results emulating strong non-linear relations with an error of about 5 % except for the shorter wavelengths around 300 nm. It is demonstrated that dominant spectral patterns can be successfully reproduced with the models within the level of radiometric calibration accuracy of GEMS, but a limitation remains when it comes to finer spectral features. When applying the reproduced spectra to retrieval processes of cloud and ozone, cloud centroid pressure shows an error of around 1 %, while total ozone column density shows relatively higher variance. As an initial step reproducing spectral patterns for bad pixels, the current study provides the potential and limitations of machine learning methods to improve hyperspectral measurements from the geostationary orbit.

As one of geostationary earth orbit constellation for environmental monitoring over the next decade, the Geostationary Environment Monitoring Spectrometer (GEMS) has been designed to observe the Asia-Pacific region to provide information on atmospheric chemicals, aerosols, and cloud properties. In order to continuously monitor sensor performance after its launch in early 2020, we suggest in this paper deep convective clouds (DCCs) as a possible target for the vicarious calibration of the GEMS, the first ultraviolet and visible hyperspectral sensor onboard a geostationary satellite. The Tropospheric Monitoring Instrument (TROPOMI) and the Ozone Monitoring Instrument (OMI) are used as a proxy for GEMS, and a conventional DCC-detection approach applying a thermal threshold test is used for DCC detection based on collocations with the Advanced Himawari Imager (AHI) onboard the Himawari-8 geostationary satellite. DCCs are frequently detected over the GEMS observation area at an average of over 200 pixels within a single observation scene. Considering the spatial resolution of the GEMS (3.5 $\times$ 8 km²), which is similar to the TROPOMI and its temporal resolution (eight times a day), the availability of DCCs is expected to be sufficient for the vicarious calibration of the GEMS. Inspection of the DCC reflectivity spectra estimated from OMI and TROPOMI data also shows promising results. The estimated DCC spectra are in good agreement within a known uncertainty range with comparable spectral features even with different observation geometries and sensor characteristics. When DCC detection is improved further by applying both visible and infrared tests, the variability of DCC reflectivity from TROPOMI data is reduced from 10% to 5%. This is mainly due to the efficient screening out of cold, thin cirrus clouds in the visible test and of bright, warm clouds in the infrared test. Precise DCC detection is also expected to contribute to the accurate characterization of cloud reflectivity, which will be investigated further in future research.

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, current study introduces an empirical correction approach that uses the correlation between 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 against the solar reference spectrum decrease 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.

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.

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.