Search Results (839)

Retrieving LST from infrared spectral observations is challenging because it needs separation from emissivity in surface radiation emission, which is feasible only when the state of the surface–atmosphere system is known. Thanks to its high spectral resolution, the Infrared Atmospheric Sounding Interferometer (IASI) instrument onboard Metop polar-orbiting satellites is the only sensor that can simultaneously retrieve LST, the emissivity spectrum, and atmospheric composition. Still, it cannot penetrate thick cloud layers, making observations blind to surface emissions under cloudy conditions, with surface and atmospheric parameters being flagged as voids. The present paper aims to discuss a downscaling–fusion methodology to retrieve LST missing values on a spatial field retrieved from spatially scattered IASI observations to yield level 3, regularly gridded data, using as proxy data LST from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) flying on Meteosat Second Generation (MSG) platform, a geostationary instrument, and from the Advanced Very High-Resolution Radiometer (AVHRR) onboard Metop polar-orbiting satellites. We address this problem by using machine learning techniques, i.e., Gradient Boosting, Random Forest, Gaussian Process Regression, Neural Network, and Stacked Regression. We applied the methodology over the Po Valley region, a very heterogeneous area that allows addressing the trained models’ robustness. Overall, the methods significantly enhanced spatial sampling, keeping errors in terms of Root Mean Square Error (RMSE) and bias (Mean Absolute Error, MAE) very low. Although we demonstrate and assess the results primarily using IASI data, the paper is also intended for applications to the IASI follow-on, that is, IASI Next Generation (IASI-NG), and much more to the Infrared Sounder (IRS), which is planned to fly this year, 2025, on the Meteosat Third Generation platform (MTG). Full article

In 2020, BDS-3 began broadcasting high-precision positioning correction products through B2b signals, effectively addressing the limitations of ground-based augmentation. However, challenges such as the “south wall effect” from geostationary orbit (GEO) satellites, issues of data (IOD) mismatch, and signal priority conflicts often result in interruptions and anomalies during real-time positioning with the B2b service. This paper proposes a continuous B2b-PPP (B2b signal-based Precise Point Positioning) model that incorporates signal-in-space range error (SISRE) residuals and predictions for B2b orbits and clock corrections to achieve seamless, high-precision continuous positioning. In our experiments, we first analyze the characteristics of B2b SISRE for both BDS-3 and GPS. We then evaluate the positioning accuracy of several models, B2b-PPP, EB2b-PPP, PB2b-PPP, EB2bS-PPP, and PB2bS-PPP, through simulated and real dynamic experiments. Here, ‘E’ indicates the direct utilization of the previous observation corrections from B2b before the signal interruption, ‘P’ represents B2b prediction products, and ‘S’ signifies the incorporation of the SISRE residuals. The results show that EB2b-PPP exhibits significant deviations as early as 10 min into a B2b signal interruption. Both PB2b-PPP and EB2bS-PPP demonstrate comparable performances, with PB2bS-PPP emerging as the most effective method. Notably, in real dynamic experiments, PB2bS-PPP maintains positioning accuracy in the E/N directions like B2b-PPP, even after 40 min of signal interruption, ensuring continuous and stable positioning upon signal restoration. This achievement significantly enhances the capability for high-precision continuous positioning based on B2b signals. Full article

Wind field retrieval, a crucial component of weather forecasting, has been significantly enhanced by recent advances in deep learning. However, existing approaches that are primarily focused on wind speed retrieval are limited by their inability to achieve real-time, full-coverage retrievals at large scales. To address this problem, we propose a novel multi-scale fusion retrieval (MFR) method, leveraging geostationary observation satellites. At the mesoscale, MFR incorporates a cloud-to-wind transformer model, which employs local self-attention mechanisms to extract detailed wind field features. At large scales, MFR incorporates a multi-encoder coordinate U-net model, which incorporates multiple encoders and utilises coordinate information to fuse meso- to large-scale features, enabling accurate and regionally complete wind field retrievals, while reducing the computational resources required. The MFR method was validated using Level 1 data from the Himawari-8 satellite, covering a geographic range of 0–60°N and 100–160°E, at a resolution of 0.25°. Wind field retrieval was accomplished within seconds using a single graphics processing unit. The mean absolute error of wind speed obtained by the MFR was 0.97 m/s, surpassing the accuracy of the CFOSAT and HY-2B Level 2B wind field products. The mean absolute error for wind direction achieved by the MFR was 23.31°, outperforming CFOSAT Level 2B products and aligning closely with HY-2B Level 2B products. The MFR represents a pioneering approach for generating initial fields for large-scale grid forecasting models. Full article

In the face of rapidly evolving communication technologies and increasing user demands, traditional terrestrial networks are challenged by the need for high-quality, high-speed, and reliable communication. This paper explores the integration of heterogeneous satellite networks (HSN) with emerging technologies such as Mobile Edge Computing (MEC), in-network caching, and Software-Defined Networking (SDN) to enhance service efficiency. By leveraging dual-layer satellite networks combining Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellites, the study addresses resource allocation and interference coordination challenges. This paper proposes a novel resource allocation and interference coordination strategy for dual-layer satellite networks integrating LEO and GEO satellites. We formulate a mathematical optimization problem to optimize resource allocation while minimizing co-channel interference and develop an ADMM-based distributed algorithm for efficient problem-solving. The proposed scheme enhances service efficiency by incorporating MEC, in-network caching, and SDN technologies into the satellite network. Simulation results demonstrate that our proposed algorithm significantly improves network performance by effectively managing resources and reducing interference. Full article

This paper presents the development and outcomes of a cost-effective satellite ground station designed as a learning tool for satellite communication and wireless communication education. The study investigates accessible satellites and the methods for accessing them. The developed ground station has the capability to access satellites in the V, U, and L frequency bands, allowing it to receive a variety of satellite data. This includes full-disk meteorological images, high-resolution multispectral images, and scientific data from payloads of satellites in both low Earth orbit (LEO) and geostationary orbit (GEO). The ground station demonstrates capabilities similar to those of large organizations but at a significantly lower cost. This is achieved through a process of identifying educational requirements and optimizing the system for cost-efficiency. This paper presents the design demonstration, actual construction of the ground station, and results. Additionally, it compiles characteristics from real signal reception experiences from various satellites. Full article

Air temperature ($T_a$) measurements are crucial for characterizing phenomena like the urban heat island (UHI), which can create critical conditions in cities during summer. This study aims to develop a machine learning-based model, namely gradient boosting, to estimate $T_a$ from geostationary satellite LST data and to apply these estimates to investigate UHI dynamics. Using Rome, Italy, as a case study, the model was trained with $T_a$ data from 15 weather stations, taking multi-temporal LST values (instantaneous and lagged up to 4 h) and additional predictors. The model achieved an overall RMSE of 0.9 °C. The resulting $T_a$ fields, with a 3 km spatial and hourly temporal resolution, enabled a detailed analysis of UHI intensity and dynamics during the summers of 2019–2020, significantly enhancing the spatial and temporal detail compared to previous studies based solely on in situ data. The results also revealed a slightly higher nocturnal UHI intensity than previously reported, attributed to the inclusion of rural areas with near-zero imperviousness, thanks to the complete mapping of $T_a$ across the domain now accessible. Full article

The geostationary meteorological satellite Fengyun-4A (FY-4A) has rapidly advanced, generating abundant high spatiotemporal resolution atmospheric precipitable water vapor (PWV) products. However, remote sensing satellites are vulnerable to weather conditions, and these latest operational PWV products still require systematic validation. This study presents a comprehensive evaluation of FY-4A PWV products by separately using PWV data retrieved from radiosondes (RS) and the Global Navigation Satellite System (GNSS) from 2019 to 2022 in China and the surrounding regions. The overall results indicate a significant consistency between FY-4A PWV and RS PWV as well as GNSS PWV, with mean biases of 7.21 mm and −8.85 mm, and root mean square errors (RMSEs) of 7.03 mm and 3.76 mm, respectively. In terms of spatial variability, the significant differences in mean bias and RMSE were 6.50 mm and 2.60 mm between FY-4A PWV and RS PWV in the northern and southern subregions, respectively, and 5.36 mm and 1.73 mm between FY-4A PWV and GNSS PWV in the northwestern and southern subregions, respectively. The RMSE of FY-4A PWV generally increases with decreasing latitude, and the bias is predominantly negative, indicating an underestimation of water vapor. Regarding temporal differences, both the monthly and daily biases and RMSEs of FY-4A PWV are significantly higher in summer than in winter, with daily precision metrics in summer displaying pronounced peaks and irregular fluctuations. The classic seasonal, regional adjustment model effectively reduced FY-4A PWV deviations across all regions, especially in the NWC subregion with low water vapor distribution. In summary, the accuracy metrics of FY-4A PWV show distinct spatiotemporal variations compared to RS PWV and GNSS PWV, and these variations should be considered to fully realize the potential of multi-source water vapor applications. Full article

We propose a semi-empirical method—based on a filtered Markov process—to convert 10 min rain rate time series into 1 min time series, i.e., quasi-instantaneous rainfall—the latter to be used as input to the synthetic storm technique, which is a very reliable tool for calculating rain attenuation time series in satellite communication systems or for estimating runoff, erosion, pollutant transport, and other applications in hydrology. To develop the method, we used a very large data bank of 1 min rain rate time series collected in several sites with different climatic conditions. The experimental and simulated 1 min rain rate time series agree very well. Afterward, we used them to simulate rain attenuation time series at 20.7 GHz, in 35.5° slant paths to geostationary satellites. The two simulated annual rain attenuation probability distributions show very small differences. We conclude that the rain rate conversion method is very reliable. Full article

Remote sensing of land surface temperature (LST) is a fundamental variable in analyzing temperature variability in urban areas. Geostationary sensors provide sufficient observations throughout the day for a diurnal analysis of temperature, however, lack the spatial resolution needed for highly heterogeneous areas such as cities. Polar orbiting sensors have the advantage of a higher spatial resolution, enabling a better characterization of the surface while only providing one to two observations per day. This work aims at using a multi-layer perceptron-based method to downscale geostationary-derived LST based on a polar-orbit-derived one. The model is trained on a pixel-by-pixel basis, which reduces the complexity of the model while requiring fewer auxiliary data to characterize the surface conditions. Results show that the model is able to successfully downscale LST for the city of Madrid, from approximately 4.5 km to 750 m. Performance metrics between training and validation datasets show no overfitting. The model was applied to a different time period and compared to data derived from three additional sensors, which were not used in any stage of the training process, yielding a R² of 0.99, root mean square errors between 1.45 and 1.58 and mean absolute errors ranging from 1.07 to 1.15. The downscaled LST is shown to improve the representation of both the temporal variability and spatial heterogeneity of temperature, when compared to geostationary- and polar-orbit-derived LST individually. The resulting downscaled data take advantage of the high observation frequency of geostationary data, combined with the spatial resolution of polar orbiting sensors and may be of added value for the study of diurnal and seasonal patterns of LST in urban environments. Full article

Satellite remote sensing complements rain gauges and ground radars as the primary sources of precipitation data. While significant advancements have been made in spaceborne precipitation estimation since the 1960s, the emergence of multi-sensor precipitation estimation (MPE) in the early 1990s revolutionized global precipitation data generation by integrating infrared and microwave observations. Among others, Global Precipitation Measurement (GPM) plays a crucial role in providing invaluable data sources for MPE by utilizing passive microwave sensors and geostationary infrared sensors. MPE represents the current state-of-the-art approach for generating high-quality, high-resolution global satellite precipitation products (SPPs), employing various methods such as cloud motion analysis, probability matching, adjustment ratios, regression techniques, neural networks, and weighted averaging. International collaborations, such as the International Precipitation Working Group and the Precipitation Virtual Constellation, have significantly contributed to enhancing our understanding of the uncertainties associated with MPEs and their corresponding SPPs. It has been observed that SPPs exhibit higher reliability over tropical oceans compared to mid- and high-latitudes, particularly during cold seasons or in regions with complex terrains. To further advance MPE research, future efforts should focus on improving accuracy for extremely low- and high-precipitation events, solid precipitation measurements, as well as orographic precipitation estimation. Full article

The ever-increasing capacity of numerical weather prediction (NWP) models requires accurate flow information at higher spatial and temporal resolutions. The atmospheric motion vectors (AMVs) extracted from the Advanced Geostationary Radiation Imager (AGRI) mounted on the Fengyun-4A (FY-4A) satellite can provide information about atmospheric flow fields on small scales. This study focused on the assimilation of FY-4A AMVs and its impact on forecasts in the regional NWP system of the China Meteorological Administration—Beijing (CAM-BJ). The statistical characterization of FY-4A AMVs was firstly analyzed, and an optimal observation error in each vertical level was obtained. Three groups of retrospective runs over a one-month period were conducted, and the impact of assimilating the AMVs with different strategies on the forecasts of the CMA-BJ system were compared and evaluated. The results suggested that the optimal observation errors reduced the standard deviation of the background departures for U and V wind, leading to an improvement in the standard deviation in the corresponding analysis departures of about 8.3% for U wind and 7.3% for V wind. Assimilating FY-4A AMV data with a quality indicator (QI) above 80 and the optimal observation errors reduced the error of upper wind forecast in the CMA-BJ system. A benefit was also obtained in the error of surface wind forecast after 6 h of the forecasts, although it was not significant. For rainfall forecast with different thresholds, the score skills increased slightly after 6 h of the forecasts. There was an overall improvement for the overprediction of 24 h accumulated precipitation forecast including the AMVs, even when conventional observations were relatively rich. The application of FY-4A AMVs with a QI > 80 and adjustment to observation errors has a positive impact on the upper wind forecast in the CMA-BJ system, improving the score skill of rainfall forecasting. Full article

Land surface temperature (LST) is a critical parameter for understanding climate change and maintaining hydrological balance across local and global scales. However, existing satellite LST products face trade-offs between spatial and temporal resolutions, making it challenging to provide all-weather LST with high spatiotemporal resolution. In this study, focusing on Chengdu city, a framework combining a spatiotemporal fusion model and machine learning algorithm was proposed and applied to retrieve hourly high spatial resolution LST data from Chinese geostationary weather satellite data and multi-scale polar-orbiting satellite observations. The predicted 30 m hourly LST values were evaluated against in situ LST measurements and Sentinel-3 SLSTR data on 11 August 2019 and 21 April 2022, respectively. The results demonstrate that validation based on the in situ LST, the root mean squared error (RMSE) of the predicted LST using the proposed framework are around 0.89 °C to 1.23 °C. The predicted LST is highly consistent with the Sentinel-3 SLSTR data, and the RMSE varies from 0.95 °C to 1.25 °C. In addition, the proposed framework was applied to Xi’an City, and the final validation results indicate that the method is accurate to within about 1.33 °C. The generated 30 m hourly LST can provide important data with fine spatial resolution for urban thermal environment monitoring. Full article

Space-based observations of the total lunar eclipse on 21 January 2019 were conducted using the geostationary Earth-orbiting satellite Gaofen-4 (GF-4). This study represents a pioneering effort to address the observational gap in full-disk lunar eclipse photometry from space. With its high resolution and ability to capture the entire lunar disk, GF-4 enabled both quantitative and qualitative analyses of the variations in lunar brightness, as well as spectra and color changes, across two spatial dimensions, from the whole lunar disk to resolved regions. Our results indicate that before the totality phase of the lunar eclipse, the irradiance of the Moon diminishes to below approximately 0.19% of that of the uneclipsed Moon. Additionally, we observed an increase in lunar brightness at the initial entry into the penumbra. This phenomenon is attributed to the opposition effect, providing scientific evidence for this unexpected behavior. To investigate detailed spectral variations, specific calibration sites, including the Chang’E-3 landing site, MS-2 in Mare Serenitatis, and the Apollo 16 highlands, were analyzed. Notably, the red-to-blue ratio dropped below 1 near the umbra, contradicting the common perception that the Moon appears red during lunar eclipses. The red/blue ratio images reveal that as the Moon enters Earth’s umbra, it does not simply turn red; instead, a blue-banded ring appears at the boundary due to ozone absorption and the lunar surface composition. These findings significantly enhance our understanding of atmospheric effects on lunar eclipses and provide crucial reference information for the future modeling of lunar eclipse radiation, promoting the integration of remote sensing science with astronomy. Full article

Exposure to space radiation substantially degrades satellite systems, provoking severe partial or, in some extreme cases, total failures. Electrostatic discharges (ESD), single event latch-up (SEL), and single event upsets (SEU) are among the most frequent causes of those reported satellite anomalies. The impact of space radiation dose on satellite equipment has been studied in-depth. This study conducts a statistical analysis to explore the relationships between low-Earth orbit (LEO) and geostationary orbit (GEO) satellite anomalies and particle concentrations, solar and geomagnetic activity in the period 2010–2022. Through a monthly and daily timescale analysis, the present work explores the temporal response of space disturbances on satellite systems and the periods when satellites are vulnerable to those disturbances. Full article

The latest-generation geostationary meteorological satellite, Fengyun-4B (FY-4B), equipped with the Geostationary Interferometric Infrared Sounder (GIIRS), offers high-spatiotemporal-resolution three-dimensional temperature structures. Its deployment serves as a critical complement to atmospheric temperature profile (ATP) observation in the Tibetan Plateau (TP). Based on radiosonde observation (RAOB) and the fifth-generation ECMWF global climate atmospheric reanalysis (ERA5), this study validates the availability and representativeness of FY-4B/GIIRS ATP products in the eastern TP region. Due to the issue of satellite zenith, this study focuses solely on examining the eastern TP region. Under a clear sky, FY-4B/GIIRS ATP exhibits good consistency with RAOB compared to cloudy conditions, with an average root mean square error (RMSE) of 2.57 K. FY-4B/GIIRS tends to underestimate temperatures in the lower layers while overestimating temperatures in the upper layers. The bias varies across seasons. Except for summer, the horizontal and vertical bias distribution patterns are similar, though there are slight differences in values. Despite the presence of bias, FY-4B/GIIRS ATP maintains a good consistency with observations and reanalysis data, indicating commendable product quality. These results demonstrate that it can play a vital role in augmenting the ATP observation network limited by sparse radiosonde stations in the eastern TP, offering crucial data support for numerical weather prediction, weather monitoring, and related meteorological research in this region. Full article