The Geostationary Environment Monitoring Spectrometer (GEMS), the first ultraviolet-visible hyperspectral imager in geostationary orbit, has been operating since its launch in February 2020. This paper evaluates and discusses the on-orbit performance of GEMS using 4.5 years of irradiance measurements (300-500 nm), with particular emphasis on the long-term changes in its radiometric accuracy and spectral stability. As part of this assessment, we examine various super-Gaussian forms used for deriving on-orbit slit functions and conclude that applying symmetry constraints to both shape and width parameters enhances fitting stability and accuracy. During the initial phase of operation, the full width at half maximum (FWHM) of the derived slit functions ranged from 0.56 to 0.58 nm across spatial and spectral dimensions. The wavelength shift remains spatially uniform and varies smoothly within ± 0.05 nm, showing a consistent, repeating pattern in 300-400 nm and 400-500 nm. However, over time, instrument degradation has led to pronounced spectral and spatial variations, particularly at shorter wavelengths. Slit widths (FWHM) at the center pixels fluctuate between 0.54 and 0.60 nm, and a “frown-shaped” spatial pattern emerges in the wavelength shift. Radiometric offsets exhibit a steady increase—approximately 5 % per year at shorter wavelengths below 330 nm and 0.2 % above 460 nm. By 2024, irradiance values are up to 35 % lower near 325 nm and about 10 % lower above 460 nm compared to the solar reference. To isolate fine-scale uncertainties, we further evaluate the fitting residuals between the measured irradiance and the radiometrically adjusted solar reference. As the degradation progressed, residuals in 310-330 nm increased sharply from 0.4 % in 2020 to 4 % in 2024, while fitting uncertainties nearly doubled in 330-360 nm. This degradation feature indicates the emergence of instrumental artifacts in spectral structures that are unrelated to physical solar absorption features. Such artifacts are most pronounced at the central pixels and have gradually extended toward the southern pixels and adjacent spectral regions throughout the mission. Beyond 400 nm, radiometric offsets occur on a broadband scale, with no noticeable degradation in the fine spectral structures.

Accurate retrieval of temperature and humidity profiles in the lower troposphere over land is crucial for improving weather forecasts and climate analyses. However, conventional retrieval methods using satellite infrared measurements suffer from limited accuracy near the surface due to insufficient sensitivity and errors in background profiles. This study develops a retrieval scheme for the Advanced Meteorological Imager (AMI) onboard the 2nd geostationary satellite of Korea, with a particular focus on improving the accuracy of profiles in the lower troposphere over land. The retrieval is based on the optimal estimation method, with key advancements introduced in both the state vector and background fields. Specifically, near-surface observations, including 2-meter temperature and humidity, from the Automated Surface Observing System (ASOS) were incorporated to adjust the lower boundary of the background profile derived from the Local Data Assimilation and Prediction System (LDAPS) forecast. Additionally, to further improve retrievals near the surface, the state vector was extended to include surface skin temperature and surface emissivity for AMI infrared channels, enabling simultaneous retrieval of these variables and atmospheric profiles. Experiments conducted for summer and winter of 2022 show that the proposed method significantly improves retrieval accuracy, particularly during summer. Based on radiosonde validation, the new retrieval method reduces the RMSE relative to the LDAPS forecast by approximately 29% for temperature and 21% for humidity below 850 hPa, whereas the conventional LDAPS-only method achieves reductions of about 5% and 1%, respectively. These results highlight the potential of integrating real-time ground-based observations with the retrieval system based on geostationary satellite data to produce more accurate lower tropospheric profiles over land, leading to increased reliability of satellite-based products.

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.

Microwave sounder observations are essential for numerical weather prediction (NWP) systems, but utilizing channels sensitive to surface over sea ice has been challenging due to difficulties in estimating the sea ice surface radiance. This study presents a pre-processing method to assimilate near-surface microwave sounding observations over winter sea ice, including an estimation of a real-time surface emissivity from satellite radiance and a bias correction scheme to minimize the radiance discrepancy between observation and model simulation. Our results show that the radiance simulated using dynamic emissivity exhibits a much better agreement with the measured one, although a significant negative bias of about 0.61 to 1.18 K remains over the winter sea ice. Thus a new bias correction procedure, based on the regression relationships between the residual bias and potential bias sources such as the surface temperature and surface emissivity, is added. When it is applied, the remaind bias were successfully estimated. Moreover, the sea ice observations from all temperature sounding channels have been better utilized in the Korean Integrated Model. The additional information on the polar regions has increased the analysis increment and reduced the ensemble spread. In addition, a neutral to slightly positive impact in temperature analysis errors in layers sensitive to surface radiance encourages further utilization of microwave sounder data over sea ice.

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.

Observations from geostationary (GEO) satellites with high-spatial and temporal resolutions have been playing a key role in building and improving global weather monitoring systems. In support of the calibration of GEO satellites, the bias characteristics in the infrared channels of four operational GEO imagers—the Advanced Meteorological Imager (AMI) on board the Geostationary Korea Multi Purpose Satellite-2A, the Advanced Himawari Imager (AHI) on the Himawari-8, the Advanced Baseline Imager (ABI) on the Geostationary Operational Environmental Satellite-16, and the Spinning Enhanced Visible and Infrared Imager flying with the Meteosat-11—are examined using two operational numerical weather prediction (NWP) models. The analysis fields from the Unified Model (UM) employed at the Korea Meteorological Administration, Seoul, South Korea, as well as the ECMWF model reanalysis (ERA5) fields are utilized for the simulation of multiple satellites using the same radiative transfer model (RTM) and the observation minus simulation statistics of the four imagers are analyzed over the clear-sky ocean as a function of time, space, observation angles, and scene temperatures. Overall, the biases of the four advanced GEO imagers demonstrate very similar characteristics to each other, although there are a few rather notable exceptions such as a striping issue in AMI, AHI, and ABI. Additionally, this study highlights the synergistic benefits of the intercalibration by revealing features specific to a particular instrument and also by indicating uncertainties of the RTM and the NWP models. It also reveals the relative performance of NWP models, showing that UM fields are overall wetter than ERA5.

A new algorithm has been developed to retrieve total column ozone (TOZ) from the infrared measurements of a geostationary satellite imager such as the Advanced Himawari Imager (AHI) aboard Himawari-8 and the advanced meteorological imager flying with the second generation geostationary satellite of South Korea. Based on a nonlinear optimal estimation method, this operational algorithm iteratively retrieves TOZ from AHI thermal measurements under clear-sky conditions as well as over low clouds at 10-min intervals with 6-km spatial resolutions. The first guess for ozone is prepared by combining a monthly climatology with the daily updated TOZ from polar orbiting satellites, while the first guess for the temperature and humidity profiles are from a numerical weather prediction model. AHI contains only one ozone absorption channel (9.6 μm), providing information contents (degrees of freedom for signal) of about 0.98 for the ozone retrieval. However, the study shows that the retrieved TOZ successfully captures the temporal and latitudinal variations of ozone flow particularly at mid- and high-latitudes. A validation using the Ozone Monitoring Instrument, Infrared Atmospheric Sounding Interferometer (IASI), and ozonesondes shows that the AHI TOZ are in a good agreement with the three references with mean difference of 2.5%, -0.5%, and 2.6%, respectively. This impressive performance is attributed to the improvements in both first-guess information and the accuracy of the retrieved temperature and humidity profiles. In particular, the good agreement shown in the comparison with the night-time IASI TOZ suggests potential benefits of using the retrieved TOZ for a continuous monitoring of global ozone evolution with high spatial resolution.