Search Results (193)

Accurately tracking the spatial and temporal variations of water vapor is indispensable for weather forecasting and climate adaptation, yet remains challenging due to the sparse coverage and discontinuity of ground-based observations. Satellite remote sensing, particularly from geostationary satellites like Meteosat Third Generation Imager-1 (MTG-I1), offers continuous, high-resolution data. To the best of our knowledge, MTG-I1 is the first geostationary satellite equipped with a near-infrared (NIR) spectral band specifically designed for detecting water vapor. To address the lack of precipitable water vapor (PWV) data derived from the Flexible Combined Imager (FCI) onboard MTG-I1, a novel semi-empirical (SE) algorithm optimized for PWV retrieval is proposed. Validation against ground-based PWV measurements using an initial test set and a temporally independent test set yielded relative errors of no more than 0.10, indicating stable retrieval performance outside the model-development period. The FCI-derived PWV retrievals were also more accurate than the corresponding MODIS PWV data. Compared to the traditional radiative transfer model (RTM)-based retrieval method, the SE method shows greater adaptability to systematic differences between the observed and RTM-simulated FCI reflectance. After correcting for radiometric degradation, the RTM-based algorithm achieves a 41% reduction in absolute error and a 47% reduction in relative error, bringing its accuracy in line with the SE algorithm. Overall, the proposed SE algorithm demonstrates superior robustness and adaptability, and can provide more reliable remote sensing PWV data to support weather forecasting and climate research. Full article

In this work, the portability of the Robust Satellite Techniques for FIRES detection and monitoring (RST-FIRES) has been preliminary experimented on the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) satellite in Rapid Scan Service (RSS) mode. Such a configuration offers 5 min of revisit time as compared with 15 min in the standard mode (0-degree). The impact in early fire detection has been assessed and quantified, also in comparison with the results of the RST-FIRES implemented on MSG/SEVIRI 0-degree data, using the official fire bulletins of the Calabria Region (Southern Italy) for the events occurred during July 2022, for which the official regional fire catalogue was available. The results obtained suggest that SEVIRI-RSS data could allow for a rather systematic earlier detection and a better sensitivity than SEVIRI 0-degree because of the improved temporal (and spatial) resolutions. These findings are remarkable in view of the next implementation of RST-FIRES on Meteosat Third Generation/Flexible Combined Imager (MTG/FCI) data, to exploit the improved spatial (2–1 km) and temporal (10–2.5 min) resolutions offered by such a new-generation geostationary mission, together with a more suitable dynamic range in the MIR spectral region (saturation at ~500 K @3.8 micron). The use of synthetic background reference fields would allow, in fact, for a straightforward RST-FIRES application to MTGI/FCI data allowing for a more effective fire early warning system. Full article

The launch of the Flexible Combined Imager (FCI) sensor aboard the Meteosat Third Generation (MTG) satellite enables higher temporal and spatial resolution for geostationary environmental monitoring. This study explores the feasibility of near-real-time fire detection using MTG-FCI data. Two unsupervised approaches are evaluated on data covering the Italian territory: a conventional threshold method, applying fixed radiometric thresholds and brightness temperature differences between 3.8 μm and 10.5 μm, and an experimental Lightweight U-Net autoencoder for anomaly detection. The autoencoder is trained exclusively on fire-free imagery, with fires identified as statistical anomalies in the reconstruction error, refined through local and global z-score analysis. Validation combines high-resolution Sentinel-2 imagery, Fire Radiative Power (FRP) and data from European Forest Fire Information System (EFFIS). Results demonstrate that MTG-FCI can trigger active fire alerts prior to polar overpasses in $67.32\%$ of the synchronized cases, providing a median early detection lead time of 21.00 min and reaching an advance of up to approximately 6 h in exceptional instances. While the spatial resolution limits detailed fire-front mapping, the high temporal resolution enables a robust near-real-time alerting system, providing enhanced detection of transient fire events that are not captured by lower-frequency polar-orbiting sensors. Full article

We analyze the evolution of atmospheric and surface physical properties over the region of the Earth observed by the Meteosat Second Generation (MSG) satellites during the period 2005–2024. Long-term changes are detected in the observed radiances, with a decrease in the solar domain (−1.3%) and an increase in the thermal infrared domain (+0.4%), consistent with trends reported by independent broadband radiometers such as CERES. The outgoing solar radiance (OSR) exhibits a marked decline, which we associate with a reduction in low-level cloud cover within the nominal Meteosat field of view (MFoV) centered at 0° longitude. Changes in atmospheric CO₂ concentration also contribute to the observed radiative imbalance at the top of the atmosphere (TOA). Instrument calibration stability and inter-satellite homogenization across the MSG series are explicitly addressed, enabling the detection of robust interdecadal signals. By subdividing the MFoV into 60 regional sectors, we characterize spatial variations in cloud amount at low and high atmospheric levels and relate these changes to regional TOA radiative imbalances and concurrent variations in Atlantic sea surface temperature (SSTs). The spectral information provided by SEVIRI allows a more detailed attribution of radiative changes than broadband observations alone from other instruments. In particular, radiances measured in the atmospheric split-window region near 11 µm are shown to be sensitive to variations in low-tropospheric humidity, which exhibits a widespread decadal-scale increase. The results indicate a close coupling between cloud-cover changes, radiative fluxes, and SST evolution on the recent interdecadal time scale. The observed decrease in low-level total cloud cover is independently in line with ECMWF ERA5 reanalysis data. These findings highlight the value of long, stable geostationary observations for investigating atmosphere–ocean interactions and their role in regional climate variability. Full article

This study introduces a novel approach to 3-hourly and daily precipitation estimation over northern Algeria. The novel approach benefits from the classification capabilities of Random Forest (RF) and the predictive power of Convolutional Long Short-Term Memory (ConvLSTM) regression, with multi-temporal observations from the SEVIRI radiometer onboard the Meteosat Second Generation (MSG) satellite. The approach is a two-stage process: A Random Forest classifier is first used to provide a probabilistic characterization of precipitation occurrence and rainfall regimes. The ConvLSTM model then applies spatio-temporal regression to estimate rainfall intensities by analyzing multi-channel temporal sequences. The hybrid model produces spatially and temporally consistent precipitation fields by taking advantage of the spatio-temporal correlations of meteorological events, with the aim of obtaining accurate 3-hourly and daily rainfall accumulations for Northern Algeria. Results show a dramatic improvement over the reference RF-based technique, with correlation coefficients reaching 0.89 for 3-hourly accumulations and 0.91 for daily rainfall. Full article

Cyclonic extreme events have recently undergone an important boost over the Mediterranean Sea, a trend closely linked to ongoing strong climate variations. Several studies are explaining the combination of many different effects that increase the frequency of mesoscale vortices’ intensification, namely Mediterranean tropical-like cyclones (TLCs), until the stage of Medicanes. Among these effects, processes like sea–atmosphere energy exchanges, baroclinic instability, and the release of latent heat lead to the intensification of these systems into fully tropical-like structures. This study investigates the formation and development of Ianos, the most intense Mediterranean tropical-like cyclone recorded in recent years, which affected the Ionian Sea and surrounding regions in September 2020. Using satellite observations and remote sensing data, the study applies a dual approach to characterise the system evolution across the spatial and temporal scales. Firstly, proper orthogonal decomposition (POD) is exploited to assess temperature and pressure fluctuations derived from the geostationary database of Meteosat Second Generation (MSG-11)/SEVIRI. POD allows for the identification of dominant modes of variability and the quantification of energy distribution across different spatial structures during the cyclone’s lifecycle. The decomposition reveals that a small number of orthogonal modes capture a significant proportion of the total variance, highlighting the emergence and persistence of coherent structures associated with the cyclone’s core and peripheral convection. To support scale-dependent energy organisation and dissipation within Ianos, total-period and three-period analyses were carried out, in addition to early-stage intensification patterns and implications for meteorological scale assessments. From the study on the temperatures’ spatio-temporal evolution, a comparison in the POD spectra and of the structures during the peak of intensity was carried out between the Ianos TLC and the Faraji and Freddy tropical cyclones. Additional multi-sensor data from Suomi NPP and Sentinel-3 satellites were integrated to analyse the evolution of the same parameters, also taking into account an evaluation of the vertical temperature gradient, over a 4-day period encompassing the full life cycle of Ianos. The study of the daily evolution helps investigate the spatial trends around the warm core regions, identifying the pressure minima for a comparison with the BOLAM and ERA5 databases of the mean sea level pressure. Overall, this study demonstrates the value of combining dynamic decomposition methods with high-resolution satellite datasets to gain insight into the multiscale structure and convective energetics of Mediterranean tropical-like cyclones. Some significant patterns come out from the spatial organisation of deep convection that seem to be linked to the permanent structures of atmospheric fluctuations near the warm core centre. Full article

In this paper, two improvements in precipitation classification have been performed. Supervised machine learning has demonstrated considerable performances in classification tasks. However, supervised machine learning can only be applied to labeled data. In some cases, large amounts of unlabeled data contain valuable information for better classification. In the classification of precipitation intensities from satellite images, unlabeled data constitute the majority and remain largely unexplored. To exploit both labeled and unlabeled data, a Semi-Supervised Support Vector Machine (S3VM) is implemented as the first improvement for classification results. The labeling of the limited available data is derived from radar measurements covering a small portion of the Meteosat Second Generation Satellite observations. The results show that the S3VM model outperforms the standard SVM model, with up to a 15% improvement in classification accuracy compared to the standard SVM. To achieve the second improvement, the S3VM was combined with the Firefly Algorithm (FFA) to optimize its hyperparameters. This hybridization (S3VM-FFA) enabled an even more robust performance. A comparative study showed that the S3VM-FFA approach yielded highly satisfactory results, achieving a 17% improvement in classification compared to the SVM results. Based on these classifications, precipitation quantities at different scales are estimated. Similarly to the classification results, statistical evaluation parameters indicate that the S3VM-FFA outperforms both the standard SVM and the conventional S3VM. Full article

Continuous global monitoring of volcanic activity from space requires balancing spatial and temporal resolution, a long-standing trade-off between polar-orbiting and geostationary satellites. Polar sensors such as MODIS, VIIRS, and SLSTR provide high spatial resolution (375 m–1 km) but with limited temporal coverage. In contrast, geostationary sensors like SEVIRI offer high temporal resolution (5–15 min) but with coarser spatial detail (~3 km), often missing lower-intensity thermal events. The recently launched Flexible Combined Imager (FCI) aboard the geostationary Meteosat Third Generation (MTG-I) satellite represents a major improvement, providing images every 10 min with a spatial resolution of 1–2 km, comparable to that of polar orbiters. Here, we adapted the established Remote Sensing Data Fusion (RSDF) algorithm to exploit the enhanced capabilities of FCI for detecting volcanic thermal anomalies and estimating Volcanic Radiative Power (VRP). The algorithm was applied to Mount Etna during three different eruptive phases that occurred in 2025. The VRP derived from FCI data was compared with that obtained from the geostationary SEVIRI and the polar-orbiting MODIS, SLSTR, and VIIRS sensors. The results show that FCI provides a more detailed and continuous characterization of volcanic thermal output than SEVIRI, while maintaining close agreement with polar sensors. These findings confirm the capability of FCI to deliver high-frequency, high-resolution thermal monitoring, representing a major step toward operational, near-real-time volcanic surveillance from space. Full article

Fourier transform spectrometers (FTSs) are cornerstone instruments in Earth observation space missions, effectively monitoring atmospheric gases in missions such as Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), and Infrared Atmospheric Sounding Interferometer (IASI). It will also be the core instrument of Meteosat Third Generation—Sounding (MTG-S) and the future Earth Explorer (EE) mission Far-infrared Outgoing Radiation Understanding and Monitoring (FORUM). Building on this legacy, the European Space Agency (ESA) has developed an FTS instrument and an inverse model designed to estimate the radiometric and spectral performance from a set of instrumental parameters. The model and its validation using in-flight measurements of the FTS instrument Gimballed Limb Observer for Radiance Imaging of the Atmosphere (GLORIA)-Lite are described in this paper. The results indicate that the difference between the model predictions and the measured signal is less than 2% relative to the average of the measurements. Moreover, we can correctly predict the instrument’s radiometric gain and offset and reconstruct a scientific science spectrum. This model can be utilised effectively to evaluate the radiometric performance of future FTS missions. Full article

Volcanic gas emissions, particularly sulfur dioxide (SO₂), are crucial for volcano monitoring. SO₂ has a significant impact on air quality, the climate, and human health, making it a critical component of volcano monitoring programs. Additionally, SO₂ can be used to assess the state of a volcano and the progression of an individual eruption and can serve as a proxy for volcanic ash. The Tajogaite La Palma (Spain) eruption in 2021 emitted large amounts of SO₂ over 85 days, with the plume reaching Central Europe. In this study, we present the results achieved by monitoring Tajogaite SO₂ emissions from 19 September to 31 October 2021 at different acquisition times (i.e., 10:00 UTC, 12:00 UTC, 14:00 UTC, and 16:00 UTC). An optimized configuration of the Robust Satellite Technique (RST) approach, tailored to volcanic SO₂ detection and exploiting the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) channel at an 8.7 µm wavelength, was used. The results, assessed by means of a performance evaluation compared with masks drawn from the EUMETSAT Volcanic Ash RGB, show that the RST product identified volcanic SO₂ plumes on approximately 81% of eruption days, with a very low false-positive rate (2% and 0.3% for the mid/low and high-confidence-level RST products, respectively), a weighted precision of ~79%, and an F1-score of ~54%. In addition, the comparison with the Tropospheric Monitoring Instrument (TROPOMI) S5P Product Algorithm Laboratory (S5P-PAL) L3 grid Daily SO₂ CBR product shows that RST-SEVIRI detections were mostly associated with SO₂ plumes having a column density greater than 0.4 Dobson Units (DU). This study gives rise to some interesting scenarios regarding the near-real-time monitoring of volcanic SO₂ by means of the Flexible Combined Imager (FCI) aboard the Meteosat Third-Generation (MTG) satellites, offering improved instrumental features compared with the SEVIRI. Full article

A significant wildfire event occurred in Korinthos, Greece, on 22 July 2020, releasing large amounts of smoke into the atmosphere. This episode provided the opportunity to develop and apply the methodology described in this work, where the synergistic use of ground data, satellite remote sensing data and dispersion modeling is utilized to demonstrate highly accurate source detection, emission transport, and dispersion of the smoke plumes. The Fire Radiative Power (FRP) data from SEVIRI, on board Meteosat Second Generation, are used to estimate hourly fire top-down emissions. These emissions are used as input for the FLEXPART Lagrangian particle dispersion model, driven by GFS meteorological data. Simulated smoke transport is compared with TROPOMI satellite CO observations and lidar profiles from the PANhellenic GEophysical observatory of Antikythera (PANGEA) station. The model includes key atmospheric processes such as advection and deposition, providing a framework for assessing wildfire impacts on air quality and transport. The results highlight the effectiveness of combining high temporal resolution FRP data with the WARM START configuration of FLEXPART versus the Standard FLEXPART Simulation. Full article

The Flexible Combined Imager (FCI), developed as the next-generation imager for the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Meteosat Third Generation (MTG) satellite series, represents a significant advancement over its predecessor, SEVIRI, on the Meteosat Second Generation (MSG) satellites. FCI offers more spectral bands, higher spatial resolution, and faster imaging capabilities, supporting a wide range of applications in weather forecasting, climate monitoring, and environmental analysis. On 13 January 2024, the FCI onboard MTG-I1 (renamed Meteosat-12 in December 2024) experienced a critical anomaly involving the failure of its onboard Calibration and Obturation Mechanism (COM). As a result, the use of the COM was discontinued to preserve operational safety, leaving the instrument dependent on alternative calibration methods. This loss of onboard calibration presents immediate challenges, particularly for the infrared channels, including image artifacts (e.g., striping), reduced radiometric accuracy, and diminished stability. To address these issues, EUMETSAT implemented an external calibration approach leveraging algorithms from the Global Space-based Inter-Calibration System (GSICS). The inter-calibration algorithm transfers stable and accurate calibration from the Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral instrument aboard Metop-B and Metop-C satellites to FCI’s infrared channels daily, ensuring continued data quality. Comparisons with Cross-track Infrared Sounder (CrIS) data from NOAA-20 and NOAA-21 satellites using a similar algorithm is then used to validate the radiometric performance of the calibration. This confirms that the external calibration method effectively compensates for the absence of onboard blackbody calibration for the infrared channels. For the visible and near-infrared channels, slower degradation rates and pre-anomaly calibration ensure continued accuracy, with vicarious calibration expected to become the primary source. This adaptive calibration strategy introduces a novel paradigm for in-flight calibration of geostationary instruments and offers valuable insights for satellite missions lacking onboard calibration devices. This paper details the COM anomaly, the external calibration process, and the broader implications for future geostationary satellite missions. Full article

The Flexible Combined Imager (FCI) instrument aboard the Meteosat Third Generation (MTG-I) geostationary satellite, launched in December 2022 and operational since September 2024, by providing shortwave infrared (SWIR), medium infrared (MIR) and thermal infrared (TIR) data, with an image refreshing time of 10 min and a spatial resolution ranging between 500 m in the high-resolution (HR) and 1–2 km in the normal-resolution (NR) mode, may represent a very promising instrument for monitoring thermal volcanic activity from space, also in operational contexts. In this work, we assess this potential by investigating the recent Mount Etna (Italy, Sicily) eruption of February–March 2025 through the analysis of daytime and night-time SWIR observations in the NR mode. The time series of a normalized hotspot index retrieved over Mt. Etna indicates that the effusive eruption started on 8 February at 13:40 UTC (14:40 LT), i.e., before information from independent sources. This observation is corroborated by the analysis of the MIR signal performed using an adapted Robust Satellite Technique (RST) approach, also revealing the occurrence of less intense thermal activity over the Mt. Etna area a few hours before (10.50 UTC) the possible start of lava effusion. By analyzing changes in total SWIR radiance (TSR), calculated starting from hot pixels detected using the preliminary NHI algorithm configuration tailored to FCI data, we inferred information about variations in thermal volcanic activity. The results show that the Mt. Etna eruption was particularly intense during 17–19 February, when the radiative power was estimated to be around 1–3 GW from other sensors. These outcomes, which are consistent with Multispectral Instrument (MSI) and Operational Land Imager (OLI) observations at a higher spatial resolution, providing accurate information about areas inundated by the lava, demonstrate that the FCI may provide a relevant contribution to the near-real-time monitoring of Mt. Etna activity. The usage of FCI data, in the HR mode, may further improve the timely identification of high-temperature features in the framework of early warning contexts, devoted to mitigating the social, environmental and economic impacts of effusive eruptions, especially over less monitored volcanic areas. Full article

The relationship between global warming and heatwaves contributes to environmental risks. We investigate lake heatwaves (LHWs) in the Eastern Mediterranean, where dust intrusions are frequently observed. The dust intrusions are characterized by the arrival of warm air masses containing dust pollution from the desert. In saline lakes, LHWs caused by dust intrusions have not been investigated in previous studies. In our study we focus on this point. It was found for the first time that, in the hypersaline Dead Sea, a severe dust intrusion (aerosol optical depth of over 3) caused the formation of LHWs, as appeared in September 2015. At the water surface, the LHWs were represented by abnormally high daily maximal and minimal surface water temperature (SWT) in comparison with their seasonally varied 90th percentile thresholds for 10 consecutive days (7–17 September). The surface LHWs’ intensity was up to 3 °C. Satellite (MODIS-Terra and METEOSAT) SWT did not detect the LHWs. Surface LHWs were accompanied by subsurface LHWs down to a depth of 20 m. The subsurface LHWs lasted longer (16 days) than the surface LHWs (10 days). There was a 4-day delay between the first date of the surface LHWs (7 September) and the start date of the subsurface LHWs (11 September). The maximal intensity of the subsurface LHWs decreased with depth from 1 m (0.6 °C) down to 5 m (0.3 °C), followed by an increase (up to 0.6 °C) at the deeper layers (from 10 m to 20 m). Taking into account that, over the Eastern Mediterranean, desert dust has increased during the past several decades, one can expect frequent occurrence of dust-related intense persistent heatwaves in the Dead Sea in the coming years. This will contribute to additional water heating and further drying up of the Dead Sea. Full article

This extensive study examines the solar rooftop photovoltaic potential (RTP) over polluted cities in major geographic and economic zones of India. The study examines the climatology of solar radiation attenuation due to aerosol, clouds, architectural effects, etc. The study exploits earth observations from ground, satellite, and radiative transfer modeling (RTM) in conjunction with geographic information systems tools. The study exploits long-term observations of cloud properties from the Meteosat Second Generation (MSG) satellites operated by EUMETSAT and aerosol properties data gathered from ground-based measurements provided by AERONET. The innovation in the study is defined in two steps. Firstly, we estimated the RTP using the current state of the art in the field, which involved using suitability factors and energy output based on the PVGIS simulations and extrapolating these for effective rooftop areas of the cities. Secondly, we advanced beyond the current state of the art by incorporating roof morphological characteristics and various area share factors to assess the RTP in more realistic terms. These two steps were applied under two different scenarios. The study determined that the optimum tilt angle is equal to the cities’ latitude for installing solar PV systems. In addition, the research emphasizes the advantages for the environment while offering energy and economic losses. According to our findings, the RTP in the rural city examined in this study is 31% greater than the urban city of India under both scenarios. The research has found that the metropolitan city, which boasts a maximum rooftop area of approximately 167 km², could host a significant RTP of around 13,005 ± 1210.71 (6970 ± 751.38) MWh per year under scenario 1 (scenario 2). Overall, solar radiation losses due to aerosol effects dominate radiation losses due to cloud effects on the city scale. Amongst all polluted cities, estimated financial losses due to aerosols, clouds, and shadows are 11,241.70 million, 4439 million, and 1167.65 million rupees, respectively. Our findings emphasize the necessity of accounting for air pollution for accurate solar potential assessments in thoughtful city planning. The creative approach that utilizes publicly available data establishes a strong foundation for penetrating solar photovoltaic (PV) technology into society. This integration could significantly contribute to climate change mitigation and adaptation efforts, promoting environmentally sustainable urban development and prevention strategies. Full article