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<p>Nitrogen dioxide (NO<sub>2</sub>) and nitrogen oxide (NO), usually referred to as nitrogen oxides (NO<sub>x</sub>), are emitted into the atmosphere by anthropogenic and natural sources. The detection and monitoring of NO<sub>2</sub> plays a key role in air quality managements because of its effects on health and of its contribution to the increase of tropospheric ozone and nitrate aerosols. Unfortunately, up to now, observations were possible only once a day based on satellites in low-earth orbit (GOME, GOME-2, OMI and TROPOMI). However, from now on, it is possible to observe the diurnal variations over the Asia based on Geostationary Environment Monitoring Spectrometer (GEMS) in geostationary earth orbit. Here, we present results of tropospheric nitrogen dioxide column observations with high temporal (hourly) and spatial resolutions over major cities in Asia. In addition, we evaluate the GEMS NO<sub>2</sub> operational algorithm by comparing GEMS total and tropospheric NO<sub>2</sub> columns with independent observations from ground-based Pandora (total column) and MAX-DOAS (tropospheric column). Additionally, we retrieved the GEMS tropospheric NO<sub>2</sub> columns by subtracting the stratospheric NO<sub>2</sub> columns, which are assumed based on SLIMCAT model data and then scalded with the real GEMS observations, from the total NO<sub>2</sub> columns. Then we also compared the tropospheric NO<sub>2</sub> columns that are retrieved based on GEMS NO<sub>2</sub> operational algorithm and SLIMCAT model, respectively. Lastly, we compared the GEMS NO<sub>2</sub> with other low-earth orbit satellite instruments that include OMI and TROPOMI.</p>

Aerosol absorption (scattering) property is a key parameter for assessing aerosol radiative effects and identifying aerosol composition. However, current geostationary Earth orbit (GEO) satellite aerosol retrieval algorithms lack accuracy in estimating aerosol absorption. The Geostationary Environment Monitoring Spectrometer (GEMS) provides hyperspectral observations of Earth-reflected solar radiation from 300 nm to 500 nm, which is sensitive to aerosol absorption. However, the current aerosol retrieval algorithm for GEMS struggles to quantify aerosol loading and aerosol absorption simultaneously. Meanwhile, the Advanced Meteorological Imager (AMI) conducts band observations of Earth-reflected solar radiation from 470 nm to 1,330 nm. The longer visible wavelength bands of AMI are less sensitive to assumption errors related to aerosol absorption properties. As a result, aerosol optical depth (AOD) products retrieved from AMI are generally more stable than those from GEMS. Therefore, synergistic use of the GEMS and the AMI can be more effective than using a single instrument to obtain both aerosol loading and absorption data. Furthermore, a wide range of wavelength from UV to visible is covered by using both GEMS and AMI. This study presents sensitivity analyses and preliminary results of a synergistic retrieval algorithm for aerosol spectral absorption properties using synergistic observations from GEMS and AMI. The algorithm incorporates a Transformer-based deep learning model for radiative transfer (RT) calculations. By replacing the traditional line-by-line RT code with a deep learning model, the algorithm enables real-time RT calculations embedded within the retrieval process. This online RT calculation approach enhances the flexibility of the aerosol retrieval algorithm and reduces errors that arise from look-up table interpolation. The developed algorithm can work on other GEO-ring missions such as GeoXO, TEMPO, ABI, Sentinel-4, and FCI.

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

The GEO-ring constellation of atmospheric composition missions was established to deliver continuous, diurnally resolved observations of trace gases and aerosols at regional scales. Since the launch of GK2B in 2020, the constellation has expanded to include TEMPO in 2023 and Sentinel-4 in 2025, with additional missions planned. This growing number of operating sensors emphasizes the need for cross-mission consistency and long-term radiometric stability across the GEO-ring framework.As the first GEO-ring sensor, the Geostationary Environment Monitoring Spectrometer (GEMS) onboard GK-2B has been operating for more than five years under harsh space environmental conditions. Over this period, the optical and electronic components of the sensor have experienced degradation of up to 30%, particularly at shorter wavelengths around 300 nm. Given that this sensor has undergone the longest period of degradation, its characterization and correction provide a valuable basis for other GEO-ring sensors with similar instrumental characteristics. In this regard, this study presents post-launch calibration and evaluation methodologies for GEMS with a focus on Level 1 products, including solar irradiance and Earth reflectance. Calibration updates address long-term degradation along with angular dependence and systematic biases of the onboard solar diffuser.For validation of the updates, GEMS is inter-calibrated with both geostationary and low Earth orbit sensors, including the Advanced Meteorological Imager (AMI) onboard the twin satellite GK2A, as well as the Tropospheric Monitoring Instrument (TROPOMI) and Ozone Mapping and Profiler Suite (OMPS). Each reference instrument provides unique strengths in spatial, spectral, and temporal coverage, enabling a comprehensive assessment of GEMS performance. The validation results indicate that the updated GEMS reflectance exhibits spectral biases within 5%, except at wavelengths below 320 nm, where straylight effects dominate.These results demonstrate that the applied calibration and inter-calibration strategies effectively improve the radiometric consistency of GEMS Level 1 products. Building on these approaches, this work highlights the importance of in-flight calibration of Level 1 products for accurate Level 2 retrievals and long-term GEO-ring consistency.

With the launch of Geostationary Ocean Color Imager(GOCI) and Meteorological Imager(MI) onboard the Communication, Oceanography, and Meteorology Satellite(COMS) in 2010, hourly monitoring of various aerosol properties has been realized. Together with the plan to launch Geostationary Environment Monitoring Spectrometer(GEMS) in 2019, monitoring of trace gas concentration will be possible in high temporal and spatial resolution. In this study, results and plan to monitor atmospheric composition from geostationary earth orbit(GEO) are presented.

Accurate estimation of ground-level ozone (O₃) concentration is crucial for assessing its health impacts and devising effective control strategies. Traditional methods relying on polar-orbit satellites offer limited, single-time measurements, failing to capture the significant diurnal variability of O₃. This study utilizes the Geostationary Environment Monitoring Spectrometer (GEMS), a next-generation geostationary satellite, to retrieve hourly O₃ concentrations. The GEMS data not only accurately captures hourly O₃ levels (R² = 0.94) but also significantly enhances the precision of daily maximum 8-hour average (MDA8) O₃ estimates, particularly in semi-urban regions, with an increase in R² by over 0.10 and a reduction in absolute error exceeding 7 μg/m³. Furthermore, our analysis reveals a 30% decrease in O₃-related health risks, with both short-term and long-term mortality rates lower than previous estimates based on polar-orbit satellites. This reduction particularly notable in semi-urban and rural areas, where satellite data is more critical due to the scarcity of ground measurements compared to urban areas. These findings suggest that previous assessments may have overestimated total mortalities and urban-rural spillover effects. Our study highlights the importance of employing high temporal resolution geostationary satellites like GEMS to accurately capture O₃ diurnal variability, providing a robust foundation for health risk assessments and guiding regulatory interventions to address O₃ pollution in China.

Atmospheric water vapour plays a key role for the Earth's energy budget and temperature distribution via radiative effects and latent heat transport. Moreover, the distribution and transport of water vapour is closely linked to atmospheric dynamics on all spatiotemporal scales. In this context, monitoring of the water vapour distribution is essential for numerical weather prediction as well as for climate modelling.The Geostationary Environment Monitoring Spectrometer (GEMS) instrument on board the GEO-KOMPSAT-2B satellite offers new opportunities for observing and investigating the regional water vapour distribution over East Asia, especially phenomena such as typhoons or atmospheric rivers, and could thus represent another valuable data source, e.g. for nowcasting systems of natural hazards.In this study, we show the first total column water vapour (TCWV) results retrieved from GEMS UV/vis spectra based on the algorithm of Borger et al. (2020). In addition, we also present an update of the existing algorithm, which, for example, replaces the previous simplified determination of the a priori water vapour profile with a deep neural network. We also compare our results to different reference data sets from ground-based in situ and remote sensing observations, reanalysis models, and satellite measurements.

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

Low earth orbit (LEO) satellite system becomes an emerging non-terrestrial network solution for future radio communications. To maximize the system capacity, an LEO satellite could utilize circularly polarized planar antenna array with the frequency reuse technique to form multiple earthmoving beams. With the increasing number of LEO satellites, inline interference from the geosynchronous equatorial orbit (GEO) satellite is an important issue to be resolved when the spectrum resource is shared. With the frequency hopping phenomena in earth-moving LEO networks, the inline interference could be minimized if the GEO users also perform the similar frequency hopping via coordination between LEO and GEO satellite networks. This paper proposes eight spectrum sharing schemes for GEO satellite networks to produce the frequency hopping sequence in earth-moving LEO network. We evaluate the system capacity, fairness between LEO and GEO users, and time costs of all schemes. Simulation results validate the advantage obtained from coordination, and show that the simple greedy scheme performs as good as the optimal scheme due to fixed frequency hopping patterns.

The efficient utilization of limited spectral resources in space presents a significant challenge given the rapidly increasing number of satellites (SATs) in various orbits. To address this challenge, it is paramount to enhance spectral efficiency by facilitating inter-operation between SATs in different orbits. In this letter, we investigate the impact of dynamic spectrum access (DSA) on the spectral efficiency of a SAT network that integrates geostationary (GEO) and low Earth orbit (LEO) SATs. Specifically, in the context of DSA, the secondary LEO SATs can opportunistically reuse the frequency bands originally assigned to primary GEO SATs. To characterize the resultant spectral efficiency in a realistic time-varying SAT network topology, we analyze the signal-to-interference-plus-noise ratio (SINR) at the nadir user of a randomly selected LEO SAT and an upper bound on average throughput of the LEO SAT. Simulation results demonstrate the significant advantages of DSA in enhancing the spectral efficiency of the integrated GEO-LEO SAT network.

Understanding the dynamics of energetic electrons in the radiation belts is key to protect space borne equipment and astronauts on-board spacecraft missions. Therefore, global reconstruction of the near-Earth radiation environment should be available at all times, radial distances and geomagnetic latitudes. Low Earth Orbit (LEO) satellites provide a large data set of rapid observations of the radiation belt region over a wide range of magnetic local times (MLT). However, the use of this data set is rather complicated due to possible proton contamination of electron fluxes and the observation of electron precipitation, leading to high variability of electron measurements, considerable instrumental errors and the need for background correction. In this study, we present a new intercalibration method for satellite measurements of energetic electrons in the radiation belt region using a data assimilation approach. Our aim is to intercalibrate the electron flux measurements of the POES satellites NOAA-15,-16,-17,-18,-19 and MetOp-02 against RBSP observations for the period October 2012 to December 2013. For this, we use a reanalysis of the radiation belt region, obtained by assimilating RBSP and GOES electron data into 3-D Versatile Electron Radiation Belt (VERB-3D) code simulations via a standard Kalman filter. Since the reanalysis provides global reconstruction of the state of the system. We compare the POES/MetOp data with our reanlysis and estimate the flux ratios at each time, location and energy. These ratios are averaged over time and space to obtain energy dependent recalibration coefficients. In order to validate our results, we perform a traditional conjunction study between POES/MetOp satellites and the Van Allen probes. Similarly, we estimate flux ratios for all the found conjunctions and calculate the corresponding energy dependent recalibration coefficients. The conjunction coefficients and the DA estimated coefficients show very good agreement. Additionally, the use of data assimilation allows for improved statistics, as the number of possible ratios is considerably improved. The recalibration coefficients estimated using the our data assimilation approach leads to good resemblance and agreement between the recalibrated POES/MetOp data set and the RBSP observations.

This paper proposes a double-layered satellite network based on the rateless code, which can globally distribute the information with a few satellites. The satellite network is composed of Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) satellites. The GEO satellites which cover the low and middle latitudes serve as the backbone network, whereas the LEO satellites are considered the enhanced network to make up for the shortage of the backbone network. Due to the interruption tolerance of the rateless code, only a few satellites are employed to achieve worldwide information distribution in the design of the satellite network. The coverage performance and the average elevation angle of the user are simulated, and results demonstrate that the network can achieve globally information distribution.

Observing System Simulation Experiments (OSSEs) are a powerful tool used to assess the potential impact on numerical weather prediction skill from planned or hypothetical future observing systems. Over the last several years an international Joint OSSE collaboration has emerged centered on the use of NASA's and NOAA's data assimilation systems. A Nature Run provided by the European Centre for Medium Range Weather Forecasts (ECMWF) has undergone extensive validation, and a set of simulated reference observations have been subjected to a set of calibration experiments. One of the first candidate observing systems assessed by this system is a wind lidar based on the Global Wind Observing Sounder (GWOS) concept developed by NASA in response to the National Research Council (NRC) Decadal Survey. OSSEs were conducted at Joint Center for Satellite Data Assimilation (JCSDA) and positive impacts from GWOS on medium range weather forecast were demonstrated. For OSSEs, all major observations used for the data assimilation have to be simulated as a control observation in addition to the observations being tested by an OSSE. Simulation of control observations and OSSE calibration are the most significant initial investments for an OSSE before it can be used to evaluate the data impact of future instruments. The Nature Run data and control observation that were simulated at NOAA from the Nature Run are made available from a NASA portal and NCAR for international collaborative Joint OSSEs. Recent developments and plans for a JCSDA OSSE based on a 2012 observation system will be also described.

In recent years, low earth orbit (LEO) satellite constellation systems have been developed rapidly. However, the scarcity of satellite spectrum resources has become one of the major obstacles to this trend. LEO satellite constellation communication systems sharing the spectrum of incumbent geostationary earth orbit (GEO) satellite system is a feasible way to alleviate spectrum scarcity. Therefore, it has practical significance to study the optimization of satellite resources allocation (RA) in a spectrum sharing scenario. This paper focuses on the RA problem that LEO satellites share a GEO high throughput satellite’s spectrum in a beam-hopping (BH) manner. The GEO satellite system is served as the primary system and the LEO satellite constellation system is served as the secondary system whose frequency bands and transmitting power are strictly limited. Compared with conventional multibeam satellites, BH satellites have the advantage of flexibility in the time dimension. Therefore, we make full use of the flexibility of LEO BH satellites to realize the matching of traffic demand and traffic supply. The RA problem is decomposed into three sub-problems, namely, frequency band selection (FBS) problem, illuminated cell selection (ICS) problem, and transmitting power allocation (TPA) problem. We solve each sub-problem in order and finally form a complete RA scheme. The performance evaluation of the proposed RA scheme is carried out in real-time and simulation results show that the LEO BH satellite paired with the RA scheme we proposed has good adaptability to the uneven distribution of traffic demand in the spectrum sharing scenario.

The increasing demand for ultra-low-latency and high-throughput communication necessitates network architectures capable of ensuring reliable real-time connectivity. To address this requirement, integrated architectures combining terrestrial networks (TN) and non-terrestrial networks (NTN)—particularly hierarchical multi-layer satellite systems composed of geostationary Earth orbit (GEO), medium Earth orbit (MEO), and low Earth orbit (LEO) satellites—have attracted considerable attention. Such hierarchical systems offer global coverage, low latency, and high capacity. However, their highly dynamic topology, fast-moving satellites, and fluctuating link quality pose significant challenges to establishing efficient end-to-end routing paths. Traditional routing methods based on static paths or centralized control lack the adaptability required for such environments. To address these challenges, this paper introduces a neural hierarchical reinforcement learning (HRL)-based routing algorithm for satellite networks. The framework exploits the layered structure of GEO, MEO, and LEO satellites, enabling gateway-assisted routing and adaptive path selection. Control responsibilities are distributed across orbital layers: the GEO layer allocates hop-count budgets, the MEO layer selects feasible LEO paths, and the LEO layer forwards data packets. When inter-satellite links are unavailable, terrestrial gateways provide alternative routes. Routing decisions further account for satellite-to-satellite and satellite-to-ground link quality, with an end-to-end delay-based reward function capturing transmission, processing, and propagation effects. Simulation results show that the proposed neural hierarchical framework, supported by integrated terrestrial and non-terrestrial networking, achieves faster convergence, greater route stability, and better adaptability than conventional routing algorithms and HRL approaches.