Geostationary Earth Orbit Satellites Research Articles

Evaluation of Himawari-8/AHI land surface reflectance at mid-latitudes using LEO sensors with off-nadir observation

Land-surface reflectance (LSR) is a basic physical retrieval in terrestrial monitoring. The potential for high-frequency surface product estimation was evident in third-generation Geostationary Earth Orbit (3rd-GEO) satellites, substantially improving spectral, spatial, and temporal resolutions. Intercomparisons with LSR products from Low Earth Orbit (LEO) satellites have been employed as a common way to evaluate the LSRs of GEO satellites. However, in mid-latitude regions, comparing the LSR between two satellites is challenging due to constraints in the sun–target–sensor geometries. In this study, we proposed a method to obtain observations with consistent viewing and illumination conditions aligned with those of the Himawari-8/Advanced Himawari Imager (AHI) at mid-latitudes, by utilizing forward and backward viewing cameras from LEO sensors, such as Terra/Multi-angle Imaging SpectroRadiometer (MISR). The reflectance intercomparison revealed that the estimated AHI LSR closely matched the LSR from MISR in the red and near-infrared (NIR) bands at latitudes higher than 30°N/S during 2018–2019, with correlation coefficient (r) greater than 0.8 and a relative root mean square error (RRMSE) below 25 %. The data accuracy in the NIR bands was higher than in the red band, as indicated by a lower RRMSE. The correlation was also stronger in non-forested regions compared to forested areas, with higher r values. Additionally, screening observation pairs based on the relative azimuth angle (RAA), which assumes rotational symmetry in LSR, was examined and proved effective for GEO–LEO intercomparisons. This RAA-matching criterion enables reflectance intercomparisons across a wide longitude range at mid-latitudes, including areas like mainland China and New Zealand, where ray-matching is not applicable. The reflectance consistency demonstrated by RAA matches was comparable to that of ray matches, although the RAA-matching is constrained by timing due to the solar location. The findings from this study have potential applications for other satellites.

Observation of Post-Sunset Equatorial Plasma Bubbles with BDS Geostationary Satellites over South China

An equatorial plasma bubble (EPB) is characterized by ionospheric irregularities which disturb radio waves by causing phase and amplitude scintillations or even signal loss. It is becoming increasingly important in space weather to assure the reliability of radio systems in both space and on the ground. This paper presents a newly established GNSS ionospheric observation network (GION) around the north equatorial ionization anomaly (EIA) crest in south China, which has a longitudinal coverage of ∼30° from 94°E to 124°E. The measurement with signals from geostationary earth orbit (GEO) satellites of the BeiDou navigation satellite system (BDS) is capable of separating the temporal and spatial variations of the ionosphere. A temporal fluctuation of TEC (TFT) parameter is proposed to characterize EPBs. The longitude of the EPBs’ generation can be located with TFT variations in the time–longitude dimension. It is found that the post-sunset EPBs have a high degree of longitudinal variability. They generally show a quasiperiodic feature, indicating their association with atmospheric gravity wave activities. Wave-like structures with different scale sizes can co-exist in the same night.

Comparative assessment and performance analysis of interference mitigation techniques for co‐existent non‐geostationary and geostationary satellites

SummaryIn recent years, technological developments with user demands, reduced production, and launch costs have rapidly increased the number of Low Earth Orbit (LEO) satellites in space. Since LEO satellites use the same frequency band as existing Geostationary Earth Orbit (GEO) satellites, the interference coordination between the two satellite networks is vital. In order to minimize the co‐existent interference between these satellite networks, studies perform on different interference mitigation strategies. In this paper, analysis and comparative assessment of these interference mitigation techniques are presented for the co‐existent Non‐Geostationary Earth (NGEO) and GEO systems. More specifically, power control (PC) and spatial isolation‐based link adaptation (SILA) techniques are studied comparatively for the performance evaluation. It is shown that the communication link bandwidth is more efficiently utilized in the SILA technique when compared with the PC technique. Moreover, the multi‐objective optimization problem (MOP) approach in the SILA technique is demonstrated to be more effective when compared with the single‐objective optimization problem (SOP) approach used in the PC technique as the simultaneous prioritizing objective functions outperforms single prioritization. Finally, it is shown that when the PC technique is applied together with the SILA technique, the exclusive angle (EA) can be reduced up to 8% for 100 Mbps, and 8.5% for 200 Mbps transmission bit rates in different operational scenarios. The presented performance evaluation in this paper may help the satellite operator or decision‐maker gain insights on which mitigation technique can be used in the case of a co‐existent interference.

Assessment of Satellite Differential Code Biases and Regional Ionospheric Modeling Using Carrier-Smoothed Code of BDS GEO and IGSO Satellites

The geostationary earth orbit (GEO) represents a distinctive geosynchronous orbit situated in the Earth’s equatorial plane, providing an excellent platform for long-term monitoring of ionospheric total electron content (TEC) at a quasi-invariant ionospheric pierce point (IPP). With GEO satellites having limited dual-frequency coverage, the inclined geosynchronous orbit (IGSO) emerges as a valuable resource for ionospheric modeling across a broad range of latitudes. This article evaluates satellite differential code biases (DCB) of BDS high-orbit satellites (GEO and IGSO) and assesses regional ionospheric modeling utilizing data from international GNSS services through a refined polynomial method. Results from a 48-day observation period show a stability of approximately 2.0 ns in BDS satellite DCBs across various frequency signals, correlating with the available GNSS stations and satellites. A comparative analysis between GEO and IGSO satellites in BDS2 and BDS3 reveals no significant systematic bias in satellite DCB estimations. Furthermore, high-orbit BDS satellites exhibit considerable potential for promptly detecting high-resolution fluctuations in vertical TECs compared to conventional geomagnetic activity indicators like Kp or Dst. This research also offers valuable insights into ionospheric responses over mid-latitude regions during the March 2024 geomagnetic storm, utilizing TEC estimates derived from BDS GEO and IGSO satellites.

Integrating terrestrial and non-terrestrial networks via IAB technology: System-level design and evaluation

As the telecommunications industry embarks on the transition to Sixth-Generation (6G) networks, this paper examines the integration of Non-Terrestrial Networks (NTN), and in particular satellite backhauling, in the context of Fifth-Generation (5G) systems. The Integrated Access and Backhaul (IAB) technology, conceived as a wireless terrestrial backhauling system in the Next Generation Radio Access Network (NG-RAN), has been identified as a possible enabler for the integration of satellite nodes. Despite the work already done in this direction, the combination of IAB architectures with satellite nodes operating in both the access and backhaul side requires further evaluations on feasibility and limitations for networks integrating Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites. To this end, this work contributes providing insights on background technologies, as well as a detailed analysis of the issues and challenges arising from such integration and a definition of use cases to support narrow-band and broadband services. Furthermore, the design and implementation of a simulation tool is proposed for a performance evaluation in terms of registration time, link capacity, single-hop and end-to-end delay. Results show that the integration turns out to be feasible, even if with strong constraints coming from the satellite system rather than the IAB usage itself. Indeed, the earth-satellite link in LEO systems has a significant impact on the packet delivery time due to the discontinuous coverage. In case of GEO satellite instead, a non-terrestrial backhaul link could limit the performance of the whole system, especially at lower elevation angles.

A new data-driven framework for progressive anomaly event alerts in spacecraft based on reconstruction discrepancy

In the use of deep learning for spacecraft anomaly detection, a key issue arises from the insufficient extraction of temporal dependencies in telemetry data. This can lead to an inability to accurately discern whether distribution changes in the data are caused by substantive anomalies or merely a consequence of model underfitting. To address this issue, we design a Temporal Dependency Extraction Enhanced Autoencoder model for multi-scale learning of telemetry data. Firstly, this model incorporates Multi-Scale Temporal Dependency Extraction blocks, which integrate self-attention, autoregressive, and feed-forward networks, aimed at systematically dissecting the long-term dependencies, historical information, and complex patterns in telemetry data. Building on these blocks, our model can efficiently and accurately reconstruct telemetry data while maintaining computational efficiency. Furthermore, we utilize an anomaly quantification metric based on the smoothed Manhattan distance, combined with the Drift Streaming Peaks-over-Threshold strategy for setting anomaly thresholds, thus establishing a comprehensive and precise anomaly alerts framework. Finally, we validate our approach using a dataset from the Attitude Control System of a Geostationary Earth Orbit satellite. The experimental results show that our method not only detects anomalies earlier than traditional methods but also provides an in-depth quantitative analysis of anomaly characteristics.

Long-Term Fuel-Optimal Collision Avoidance Maneuvers with Station-Keeping Constraints

This work presents a sequential convex program method to compute fuel-optimal collision avoidance maneuvers for long-term encounters. The low-thrust acceleration model is used to account for the control, but the method can compute high-thrust maneuvers by increasing the maximum available acceleration. Dealing with the long-term conjunction poses additional challenges compared to the short-term problem because the encounter is not instantaneous. Thus, under the assumption of Gaussian statistics, the probability of collision is replaced by a simpler metric, the instantaneous probability of collision (IPoC), and a keep-out zone constraint is formulated as a continuous condition to be respected throughout the time frame of interest. The robustness of the solution is improved by introducing a constraint on the sensitivity of IPoC. Furthermore, the collision avoidance problem is coupled with the classical station-keeping requirement for geostationary Earth orbit satellites and with a return to the nominal orbit condition for low Earth orbit satellites. Even though no guarantee is given for the recovery of the global optimum solution, numerical simulations in different orbital regimes show that the proposed approach can yield a local fuel-optimal solution with a run time suitable for autonomous applications.

Near-real-time hourly PM2.5 prediction over East Asia using geostationary satellite products and machine learning

Airborne ground-level particulate matter of size <2.5 μm (PM2.5) is a potential health hazard, even with short-term exposure. To quantify the amount of ambient PM2.5, there have been many advances in the use of satellite observations in estimating PM2.5 concentrations over broader areas. Although hindcasts have successfully estimated PM2.5 concentrations at locations with no ground-based observations, nowcasts are crucial in preventing potential exposure to PM2.5 in real time. In this study, a near-real-time hourly PM2.5 prediction framework was designed using a geostationary earth orbit satellite, Geostationary Ocean Color Imager-II (GOCI-II) aerosol products, and random forest (RF) models to yield hourly PM2.5 predictions over East Asia 10 times daily at 2.5 km resolution. The framework includes an oversampling stage, which increases the number of infrequent PM2.5 occurrences. The algorithm was tested over a one-year period, during which predictions and observations were strongly correlated (R2 = 0.79) with a relative root mean square error of 41.99%. The results were comparable to or better than those of previous hindcast models. Uncertainties in prediction stemmed mainly from the number of valid predictions and the absolute volume of training samples. Due to the high temporal and spatial resolution, predicted PM2.5 concentrations successfully filled in regions with no ground-based stations. The products also identified concentration gradients within sub-districts, and their temporal changes during daytime. Prediction with a model with a training dataset from an hour ago still produced robust results. The products may be used in releasing health hazard alerts through real-time monitoring of air quality.

Ionospheric behaviors and characteristics in Asian sector during the April 2023 geomagnetic storm with multi-instruments observations

Geomagnetic storms frequently affect satellite navigation, communication and satellite orbits. Monitoring and understanding the ionospheric disturbances and responses to geomagnetic storms are crucial. The detailed ionospheric responses and physical mechanisms to various geomagnetic storms, however, have not yet been extensively studied. In this paper, the ionospheric variation behaviors and features following the April 2023 magnetic storm along the Asian sector are thoroughly studied using multi-instrument observation data, including the Global Navigation Satellite System (GNSS), ionosonde, and other satellites. Large-scale Traveling Ionospheric disturbances (LSTIDs) are observed from BeiDou Geostationary Earth Orbit (GEO) satellites, GPS and GLONASS. LSTIDs traveled with a speed of 760–1300 m/s from high latitude region to low latitude region with a period of about 40 min. The equatorial propagating LSTIDs were generated by coronal mass ejections (CMEs), which occurred in April 2023 with periodic energy input from the auroral area. The poleward LSTIDs are also observed with a velocity of approximately 600–750 m/s and the period is similar. Neutral wind also influenced the characteristics of the ionospheric response. The [O]/[N2] ratio declined during the storm, which led to the formation of the negative storm phases. The largest vertical total electron content (VTEC) is found, and the strengthened region of TEC is mainly centered between ± 20° within geographical latitude. Equatorial Ionospheric Anomaly (EIA) is also observed, which is probably influenced by the electric field. As the time goes on, the peak on the south side of the EIA is disappearing. Meanwhile, the height of the ionospheric maximum electron density rises, and the electron density falls.

A Study on Anti-Jamming Algorithms in Low-Earth-Orbit Satellite Signal-of-Opportunity Positioning Systems for Unmanned Aerial Vehicles

Low-Earth-Orbit (LEO) satellite Signal-of-Opportunity (SOP) positioning technology has gradually matured to meet the accuracy requirements for unmanned aerial vehicle (UAV) positioning in daily scenarios. Advancements in miniaturization technology for positioning terminals have also made this technology’s application to UAV positioning crucial for UAV development. However, in the increasingly complex electromagnetic environment, there remains a significant risk of degradation in positioning performance for UAVs in LEO satellite SOP positioning due to unintentional or malicious jamming. Furthermore, there is a lack of in-depth research from scholars both domestically and internationally on the anti-jamming capabilities of LEO satellite SOP positioning technology. Due to significant differences in the downlink signal characteristics between LEO satellites and Global Navigation Satellite System (GNSS) signals based on Medium Earth Orbit (MEO) or Geostationary Earth Orbit (GEO) satellites, the anti-jamming research results of traditional satellite navigation systems cannot be directly applied. This study addresses the narrow bandwidth and high signal-to-noise ratio (SNR) characteristics of signals from LEO satellite constellations. We propose a Consecutive Iteration based on Signal Cancellation (SCCI) algorithm, which significantly reduces errors during the model fitting process. Additionally, an adaptive variable convergence factor was designed to simultaneously balance convergence speed and steady-state error during the iteration process. Compared to traditional algorithms, simulation and experimental results demonstrated that the proposed algorithm enhances the effectiveness of jamming threshold settings under narrow bandwidth and high-power conditions. In the context of LEO satellite jamming scenarios, it improves the frequency-domain anti-jamming performance significantly and holds high application value for drone positioning.

Multiband photometric observations of GEO objects through Sloan filters

With the precipitous growth of the population of objects in Earth's orbit, all major space agencies and companies have made space surveillance a priority to ensure the safety of space missions. With the application of the methods of space surveillance, this article aims to characterize the population of objects in the Geostationary Earth Orbit (GEO) using optical measurements across multiple spectral bands. The study takes place in a context in which the existence of space debris poses a continuous hazard to the operations of active satellites in GEO. Photometric observations of GEO objects using Sloan filters were obtained with SCUDO, a small-aperture telescope part of the Sapienza network. Observations are divided into categories, including 11 objects: rocket bodies (4), defunct satellites (3), and active satellites (4). The spectral response of these objects has been analyzed leading to the identification of a photometric signature which was repeated for two Eutelsat satellites observed under same conditions. All the observed targets resulted in a lower magnitude in the near-infrared. Color indexes also indicate that they are redder than the Sun. This is particularly evident for SL-12 rocket bodies, whose g’-z’ reaches 1.5.

Comparison of the SRP spherical model between LEO and GEO satellites

Solar Radiation Pressure (SRP) is a phenomenon caused by the pressure exerted by solar photons on a satellite’s surface when it is exposed to sunlight. It is a form of radiation force and can significantly impact the motion and behaviour of satellites in space. SRP influences a satellite’s orbit by causing changes in its semimajor axis, eccentricity, inclination, argument of perigee, right ascension of the ascending node, and mean anomaly. SRP models are used to simulate the effects of solar radiation pressure on satellites. These models are essential for accurately predicting satellite trajectories and orbital behaviour. There are several types of SRP models, such as spherical model, flat model, box-wing model, faceted model, and analytical SRP models. This research focuses on Telkom 1 and LAPAN A1 satellites, both belonging to Indonesia and positioned in Geostationary Earth Orbit (GEO) and Low Earth Orbit (LEO) orbits, respectively. The study aims to find a comparison of the effects of SRP spherical model on LEO and GEO satellites. Our modelling shows that the semimajor axis and eccentricity are sensitive to SRP, while the inclination and right ascension of the ascending node are not significantly affected. Comparing the effects of SRP on LEO and GEO satellites, we concluded that both LEO and GEO orbit experience the most significant fluctuations in January (perihelion), likely due to the influence of solar radiation pressure.

Real-time high-precision baseline measurement of satellite formation flying based on GNSS

With the establishment of the Chinese space station, a series of space science missions will be conducted in its vicinity. One of the extended tasks for the in-orbit service of space station is the formation flying of small satellites centered around the space station. Satellite formations can perform simultaneous three-dimensional observations of the space station, generating three-dimensional images and monitoring and assessing the status of critical components of the space station. The real-time and accurate relative position measurement is one of the key issues in formation flying. It is a fundamental prerequisite for maintaining and reconfiguring formation configurations, collision avoidance and safety assurance. GNSS-based relative positioning is a low-cost and high-precision measurement method, which is not constrained by light, weather, or relative attitude of satellites. This study introduces a novel approach to determine the relative position of satellites based on single-epoch Global Navigation Satellite System (GNSS) carrier phase difference measurements. Unlike traditional methods, the proposed technique eliminates the need for precise ephemeris and clock bias files and does not require historical observation data. The algorithm avoids the complexity associated with full cycle ambiguity jumps, facilitating real-time, high-precision calculations of relative position baseline vectors. Special attention is given to the influence of Geostationary Earth Orbit (GEO) satellites in the BeiDou Navigation Satellite System (BDS), and measures are taken to mitigate their impact on measurement accuracy by adjusting their observation weights. Our newly developed GNSS-based real-time measurement module, designed for high-quality GNSS signal reception and data communication, was tested on terrestrial and simulated low-orbit platforms. Remarkably, over baselines ranging from 10 m to 9.3 km, while traditional pseudorange differential measurements showed errors up to 1.5571 m, our proposed method consistently held errors under 5 cm and ensured computational speeds within 0.1 s per epoch. High-dynamic zero-baseline tests further validated its potential, with errors less than 1 cm. The innovations encapsulated in this work, resonating with the evolving trends of miniaturized and scalable satellites, present a cost-efficient and real-time solution for inter-satellite relative position measurement. This promises a new horizon in satellite formation technology, with future endeavors focusing on on-orbit experiments.

Data formats and standards for opportunistic rainfall sensors.

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

The Analysis of Ionospheric TEC Anomalies Prior to the Jiuzhaigou Ms7.0 Earthquake Based on BeiDou GEO Satellite Data

The position between BeiDou geostationary Earth orbit (GEO) satellites and ground-based receiving stations can roughly be considered to be constant with negligible fluctuations; thus, the total electron content (TEC) data over a fixed ionospheric piercing point (IPP) can be continuously acquired, which is advantageous for monitoring ionospheric disturbances. Focused on the Jiuzhaigou Ms7.0 earthquake that occurred on 8 August 2017, the TEC data inverted by the BeiDou GEO satellite were analyzed to extract ionospheric disturbances potentially associated with the earthquake. It was found that significant anomalies in ionospheric TEC occurred 10–11 days, 6–7 days, and 1–9 h prior to the earthquake, which was mainly located in the southeast and southwest directions within about 2500 km distance from the epicenter. Comparing the spatial and temporal characteristics between the ionospheric disturbance and the radon gas near the surface, the atmospheric electric field, and the spectrum of TEC data, it was considered that the chemical and acoustic–gravity wave pathway may play an important role in the lithosphere–atmosphere–ionosphere coupling (LAIC) mechanism.

On the Importance of a Geostationary View for Tropical Cloud Feedback

AbstractThis study shows that geostationary satellites are critical to estimate the accurate cloud feedback strength over the tropical western Pacific (TWP). Cloud feedback strength was calculated by the simultaneous relation between cloud cover and sea surface temperature (SST) over the TWP [120°E–170°E, 20°S–20°N]. During 2011–2018, the cloud cover was obtained by geostationary earth orbit satellite (GEO) and low‐level earth orbit satellite (LEO) (AGEO, ALEO), and the NOAA's all‐sky SST (To) was weighted with the clear‐sky fraction observed by GEO and LEO (TwGEO; TwLEO). The linear regression coefficients between clouds and SST are very different: −7.93%K−1 (AGEO/TwGEO), −6.94%K−1 (ALEO/TwGEO), −1.35%K−1 (AGEO/TwLEO), −0.69%K−1 (ALEO/TwLEO), −0.02 %K−1 (AGEO/To), and −0.50 %K−1 (ALEO/To). Among these, only the TwGEO values provided a valid cloud feedback signal. This is because GEO's field of view is large enough to simultaneously capture cloud cover over the entire TWP.

Modeling and analysis for reaction control thruster used for station-keeping maneuvers of a GEO satellite

This paper introduces a theoretical description, force, and torque analysis of a Reaction Control Thruster (RCT) used as an actuator in Attitude Determination and Control Subsystem (ADCS). Mathematical formalization, realization, and implementation are offered to implement the RCT model using Matlab-Simulink. Furthermore, this paper proposes an accurate RCT Simulink model to control the orientation and stabilizes the Geostationary Earth Orbit (GEO) satellite within its orbit. Finally, a comparative analysis is carried out with an existing satellite platform (Eurostar-3000) based on the Root Mean Square Error (RMSE) for the delta velocity (delta-𝒱) parameter. The outcomes of the model simulation validated the effectiveness of the proposed approach.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

Data formats and standards for opportunistic rainfall sensors

Opportunistic sensors are increasingly used for rainfall measurement. However, their raw data are collected by a variety of systems that are often not primarily intended for rainfall monitoring, resulting in a plethora of different data formats and a lack of common standards. This hinders the sharing of opportunistic sensing (OS) data, their automated processing, and, at the end, their practical usage and integration into standard observation systems. This paper summarises the experiences of the more than 100 members of the OpenSense Cost Action involved in the OS of rainfall. We review the current practice of collecting and storing precipitation OS data and corresponding metadata, and propose new common guidelines describing the requirements on data and metadata collection, harmonising naming conventions, and defining human-readable and machine readable file formats for data and metadata storage. We focus on three sensors identified by the OpenSense community as prominent representatives of the OS of precipitation: Commercial microwave links (CML): fixed point-to-point radio links mainly used as backhauling connections in telecommunication networks Satellite microwave links (SML): radio links between geostationary Earth orbit (GEO) satellites and ground user terminals. Personal weather stations (PWS): non-professional meteorological sensors owned by citizens. The conventions presented in this paper are primarily designed for storing, handling, and sharing historical time series and do not consider specific requirements for using OS data in real time for operational purposes. The conventions are already now accepted by the ever growing OpenSense community and represent an important step towards automated processing of OS raw data and community development of joint OS software packages.

Reliability Analysis Based on Air Quality Characteristics in East Asia Using Primary Data from the Test Operation of Geostationary Environment Monitoring Spectrometer (GEMS)

Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O3 ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO2 ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO2 than that of OMI and TROPOMI. Based on the sources of O3 and NO2, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis.