Meteosat Third Generation: progress on space segment system feasibility studies: payload aspects

The Meteosat Third Generation (MTG) Programme is being realised through the well established and successful Cooperation between EUMETSAT and ESA. It will ensure the future continuity of MSG with the capabilities to enhance nowcasting, global and regional numerical weather prediction, climate and atmospheric chemistry monitoring data from Geostationary Orbit.

The METEOSAT Third Generation (MTG) Programme will provide the geostationary platforms for operational meteorological data acquisitions over Europe in 2018-2030. The Flexible Combined Imager (FCI) instrument is one of the MTG imager instruments and has a heritage from SEVIRI flown on the current METEOSAT Second Generation (MSG) satellites. It is a radiometer providing measurements in 16 spectral bands with a full Earth coverage every 10 minutes. For the Level 2 processing of FCI datasets the measurements have to be re-sampled on a constant reference grid in a geostationary projection – this process is referred to as rectification. The use of a three-axis stabilised platform and the scanning scheme applied to the FCI make rectification in MTG more challenging than in the MSG/SEVIRI case. Classical interpolation formulas assume a uniform sampling spacing of the measurements. However, non-uniform sampling may occur in the FCI sampling acquisition due to platform dynamics, micro-vibrations, thermo-elastic focal plane and optical distortions. In such a case, classical methods can cause significant rectification errors and interpolation algorithms, which can cope with non-uniform sampling, are required. This paper analyses the effect of non-uniform sampling in the FCI rectification process and aims to select and assess suitable resampling algorithms for the FCI L1 processing chain. Several techniques tailored to non-uniform resampling have been implemented. Performances of both uniform and non-uniform interpolation algorithms have been evaluated and compared using simulated FCI-like data samples. The analysis has been done for a nominal and a worst-case sample acquisition scenario. The presentation will show the results of our simulations with respect to the MTG requirements.

The Meteosat Third Generation (MTG) Programme will ensure the future continuity and enhancement of meteorological data from geostationary orbit as currently provided by the Meteosat Second Generation (MSG) system. The industrial prime contractor for the space segment is Thales Alenia Space (France), with a core team consortium including OHB System AG (Germany).

The transition from Meteosat Second Generation (MSG) to Meteosat Third Generation (MTG) represents a major step forward in European geostationary satellite observing capability. This work presents preparatory and preliminary results from extending the established all-sky radiance assimilation framework, originally developed for MSG SEVIRI to MTG FCI. The study emphasizes methodological continuity to ensure a seamless evolution of satellite data assimilation practices within convective-scale numerical weather prediction. Earlier all-sky assimilation experiments with SEVIRI water vapour channels established a robust framework for handling cloud-affected radiances through dynamically adaptive, cloud-dependent observation error modelling. Here, the same principles are applied to FCI water vapour channels, enabling a consistent assessment of how these observations interact with existing assimilation systems. FCI’s enhanced spatial resolution, improved radiometric accuracy, and expanded spectral sampling promise greater information content, but also introduce increased representativeness and cloud-related uncertainties, reinforcing the value of adaptive error characterisation. Preliminary 3D-EnVar experiments within the pre-operational regional high resolution ensemble prediction system of GeoSphere Austria compare the all-sky assimilation approaches for both SEVIRI and FCI radiances. Results show that the cloud-dependent observation error model previously validated with SEVIRI yields a neutral to positive impact on the short-range forecast. This demonstrates a structured pathway toward the operational exploitation of FCI observations, supporting improved short-range forecasts of high-impact weather. Progress on assimilation of visible channels from SEVIRI and FCI are also foreseen.

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.

<p>The first satellite in the Meteosat Third Generation (MTG) series was launched on 13 December 2022. It carries the state-of-the-art Flexible Combined Imager (FCI) and Lightning Imager (LI) which will provide significant improvements to geostationary observations over Europe, Africa, and surrounding regions. The FCI has 16 spectral channels, collects full disk images every 10 minutes, and provides up to 500 m spatial resolution, all notable upgrades over the imager aboard the previous Meteosat Second Generation series. And the LI will provide the first continuous optical lightning detection capability from geostationary orbit over Europe. The National Oceanic and Atmospheric Administration (NOAA) has been operating its Advanced Baseline Imager and Geostationary Lightning Mapper as part of the GOES-R satellites series over the western hemisphere since 2016. These instruments have many similarities to MTG’s and provide ideal proxy data for understanding how the new satellite observations will benefit severe storm analysis and short-term forecasting in Europe and Africa.</p> <p>This presentation will highlight the various ways that NOAA’s GOES-R satellites have improved severe storm analysis over the U.S. These include better detection of boundaries prior to convective initiation (CI), improved monitoring of cumulus fields and low-level moisture in anticipation of CI, the ability to track lightning activity, improved depiction of severe storm top features such as above anvil cirrus plumes and overshooting tops, and the ability to occasionally detect storm-scale rotation from space. In The potential of all of these will be discussed in the context of severe storm nowcasting over MTG’s domain.</p>

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. The objective of this study is to prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system within a new EnVar assimilation scheme, by developing a lightning observation operator. This study relies on pseudo LI FEA observations as LI on MTG is still to be launched in the end of 2022, meaning actual observations will be available by 2023. Since neither flashes nor the electric field are predicted by the AROME NWP system, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the NWP system. A total of 8 storm parameters were selected from a literature review to be used as proxies. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. The proxies are calculated from 1 h AROME forecasts from the assimilation cycle. Machine learning regression models are used to relate observed FEA and the simulated proxies. The training of the observation operator is performed on a dataset of 44 days and 3 additional days are used for the validation. The data are processed as a climatology over the whole domain (i.e. France) and time period. The performances of each proxy are evaluated by computing Fraction Skill Scores (FSS) between observed FEA and proxy-based FEA. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations with the AROME NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study but no proxies combination yielded better results than microphysical proxies alone. Results are also compared to those obtained with another lightning calibration, i.e., a relationship between lightning and proxies, from the literature but the simulated FEA amplitudes were systematically lower than the observed ones. Finally, different accumulation periods of the FEA had little influence, i.e., similar FSS, on the observation operator's ability to reproduce the observed FEA.

<p>Nowcasting severe weather is crucial not only to mitigate the effects of extreme weather events like storms and flash floods but also to support decision-makers on weather-dependent operations like aviation and outdoor events. Stunning pace of developments in Artificial Intelligence (AI) and increasing availability of high resolution data from different sensors motivate using AI in weather prediction, particularly in nowcasting due to its rapidly changing dynamics in short timescales.</p><p>Meteosat Third Generation (MTG) will greatly improve our capacity on nowcasting with its high resolution sensors on board. However, data-driven algorithms are needed to use the information stored within large volumes of MTG data for nowcasting in an efficient way. Therefore, we are developing AI based nowcasting algorithms fusing remote sensing data with ground based radar mosaics for nowcasting severe weather. </p><p>As MTG data are not available yet (expected to be launched in late 2022), we use data from GOES satellites as the primary data source, which have comparable sensors with MTG. Specifically, we use the Storm Event ImageRy (SEVIR) dataset in the initial phase of the study which contains more than 10000 image sequences, 20 % of which contain storm events reported by NOAA. Each of these image sequences cover 384 x 384 km in space and 4-hour in time, containing three bands from the visible and infrared spectrums, and the lightning mapper data from GOES-16 with Vertically Integrated Liquid (VIL) mosaics derived from ground-based radar. We obtained promising results to reproduce spatial variations of VIL with Generative Adversarial Network (GAN) as a baseline. We are currently developing Recurrent Neural Network (RNN) based models to reproduce temporal variations of VIL to incorporate temporal information in GAN models. Furthermore, we are using European Weather Cloud, which not only provides a strong computation infrastructure but also fosters collaboration across projects. Development of efficient AI algorithms for nowcasting severe weather using GOES data will enable the opportunity to fully use MTG data on nowcasting severe weather.</p>

With the Meteosat Third Generation (MTG) programme EUMETSAT is preparing its next generation of geostationary fleet of satellites. Together with the procurement of in total six 3-axis stabilised satellites, 4 imaging and 2 sounder spacecrafts, the ground segment has been designed under the lead of EUMETSAT and has entered its development phase. The Ground Segment will comprise various facilities, one of them being the Mission Operation System called Mission Operations Facility (MOF), procured from the prime contractor GMV and its subcontractor SciSys providing the Mission Planning and Automation functions.

The mission success of the geostationary operational satellite system Meteosat Third Generation (MTG) will significantly depend on the instrument performance in the very long wavelength infrared (VLWIR) spectral range. As far as dark current behavior, homogeneity, and operability are concerned, the VLWIR constitutes a major challenge for sensor material improvement and device development. This paper reports on the latest results on HgCdTe (MCT) VLWIR photovoltaic sensor development and characterization for possible use with MTG. In order to achieve low enough dark currents, extrinsically p-doped MCT material with various cut-off wavelengths in the long wavelength infrared (LWIR)/VLWIR has been developed and manufactured. Compared to standard intrinsic MCT, a reduction in dark current by more than an order of magnitude is achieved, meeting the challenging MTG requirements. In a (256x256) VLWIR MCT focal plane array (FPA) with an ~14.7μm cut-off wavelength at a 55K operating temperature, a dark current density of about 1pA/μm 2 is demonstrated. For a 291K reference scene and at half-well integration capacity, we obtain a noise equivalent temperature difference of (24.0±3.0)mK and a photo response of 13mV/K.

Recent European atmospheric imaging missions have seen a move towards the use of CMOS sensors for the visible and NIR parts of the spectrum. These applications have particular challenges that are completely different to those that have driven the development of commercial sensors for applications such as cell-phone or SLR cameras. This paper will cover the design and performance of general-purpose image sensors that are to be used in the MTG (Meteosat Third Generation) and MetImage satellites and the technology challenges that they have presented. We will discuss how CMOS imagers have been designed with 4T pixel sizes of up to 250 μm square achieving good charge transfer efficiency, or low lag, with signal levels up to 2M electrons and with high line rates. In both devices a low noise analogue read-out chain is used with correlated double sampling to suppress the readout noise and give a maximum dynamic range that is significantly larger than in standard commercial devices. Radiation hardness is a particular challenge for CMOS detectors and both of these sensors have been designed to be fully radiation hard with high latch-up and single-event-upset tolerances, which is now silicon proven on MTG. We will also cover the impact of ionising radiation on these devices. Because with such large pixels the photodiodes have a large open area, front illumination technology is sufficient to meet the detection efficiency requirements but with thicker than standard epitaxial silicon to give improved IR response (note that this makes latch up protection even more important). However with narrow band illumination reflections from the front and back of the dielectric stack on the top of the sensor produce Fabry-Perot étalon effects, which have been minimised with process modifications. We will also cover the addition of precision narrow band filters inside the MTG package to provide a complete imaging subsystem. Control of reflected light is also critical in obtaining the required optical performance and this has driven the development of a black coating layer that can be applied between the active silicon regions.

The Lightning Imager (LI) onboard the Meteosat Third Generation (MTG) satellites will provide total lightning observations continuously over Europe with a spatial resolution of a few kilometers. The objective of this study is to prepare the assimilation of the flash extent accumulation (FEA) measured by LI in the French storm-scale regional AROME NWP system within a new EnVar assimilation scheme, by developing a lightning observation operator. This study relies on pseudo LI FEA observations as LI on MTG is still to be launched in the end of 2022, meaning actual observations will be available by 2023. Since neither flashes nor the electric field are predicted by the AROME NWP system, the observation operator relies on proxy variables to link the flash observations to the prognostic variables of the NWP system. A total of 8 storm parameters were selected from a literature review to be used as proxies. Two different proxy types emerged from this literature review: microphysical and dynamical proxies. The proxies are calculated from 1 h AROME forecasts from the assimilation cycle. Machine learning regression models are used to relate observed FEA and the simulated proxies. The training of the observation operator is performed on a dataset of 44 days and 3 additional days are used for the validation. The data are processed as a climatology over the whole domain (i.e. France) and time period. The performances of each proxy are evaluated by computing Fraction Skill Scores (FSS) between observed FEA and proxy-based FEA. The present study suggests that microphysical proxies seem to be more suited than the dynamical ones to model satellite lightning observations with the AROME NWP system. The performances of multivariate regression models are also evaluated by combining several proxies after a feature selection based on a principal component analysis and a proxy correlation study but no proxies combination yielded better results than microphysical proxies alone. Results are also compared to those obtained with another lightning calibration, i.e., a relationship between lightning and proxies, from the literature but the simulated FEA amplitudes were systematically lower than the observed ones. Finally, different accumulation periods of the FEA had little influence, i.e., similar FSS, on the observation operator's ability to reproduce the observed FEA.

<p>Satellite observations of cloud properties are of critical importance for a number of established, operational, and emerging applications serving a wide user community. Examples include the support to nowcasting activities in National Weather Services for the anticipation of evolving severe storms, icing risk evaluation for air traffic, estimation of surface radiation for photovoltaic systems. Numerical Weather Prediction Models (NWP), whilst generally not directly assimilating cloud products, use them for model evaluation and, in the context of climate change, Thematic Climate Data Records from level-2 products can support Climate Analysis. Cloud products are also direct inputs to several level-2 retrievals such as Atmospheric Motion Vectors (AMV), for which quality cloudy pixel identification and height assessment are crucial for their use in NWP.</p><p>Similarly, satellite observations of aerosol properties provide support to regulatory and legislative activities in Member States to support air quality monitoring and climate products, particularly in case of dust storms or volcanic ash events. The Copernicus Atmosphere Monitoring Service (CAMS) facilitates the use of the data for this purpose and has a need for high quality near-real time operational aerosol products for use in data assimilation. High quality aerosol products are becoming increasingly important for NWP and climate modelling due to the influence of aerosols on the radiation budget and their role in cloud processes and precipitation.</p><p>The potential for EUMETSAT to provide state of the art cloud products will significantly increase with the Meteosat Third Generation (MTG) and the EUMETSAT Polar System programme Second Generation (EPS-SG) and with the possibility of deriving cloud properties from the Copernicus Sentinel-3 instruments. Improvements include higher spatial resolution and richer spectral coverage, which together will allow more accurate cloud detection, estimation of the cloud phase, layering, altitude and spatial inhomogeneity, at the same time maintaining continuity with the legacy products based on current instruments. </p><p>Moreover, EUMETSAT, as the only European agency providing aerosol products for near real time services, will significantly benefit from the new suite of instruments, such as dedicated aerosol polarimeter (3MI), the Multi Angle Polarimeter (MAP) from the Copernicus CO2M mission and from synergy between sensors. These new sensors will allow the exploration of the new information from multi-angle, multi-spectral polarimetric observations together with other instrument classes, to improve estimates of the aerosol optical depth, aerosol type, optical characteristics and layer height. </p><p>The talk will provide an overview of the activities within EUMETSAT to prepare for the challenges and opportunities offered by the new imagers and spectrometers on board of the upcoming geostationary and polar orbiting missions, will outline the potential for development of multi-instrument synergistic products, and discuss the plans for calibration and validation of level-2 products against surface and satellite-borne reference datasets.</p>

The precise nowcasting and forecasting of convective storms affects airport operations, as they are important to the safe use of aircraft, scheduling and affecting ground operations. The present study integrated the analysis of convective activity over Istanbul Airport from 20-21 September 2024 using single satellite imagery, ground based radar data and recently available products from the Meteosat Third Generation (MTG) for real time operability for forecast decision making.The analysis commenced with continuous monitoring utilized through combined use of EUMETView satellite imagery and ground based weather radar observations of the same meteorological event. The RGB composites of air mass derived from the satellite data, infrared brightness temperature fields at different levels and visible reflectance channels allowed for the identification of large-scale atmospheric structure, upper-level forcing and cloud-top cooling trends. Meanwhile, the radar reflectivity provide a high level of resolution to track precipitation cores, echo tops and vertical development of the storm.As the convective activity intensified, the FCI provided data that was able to allow clearer probability of cloud microphysical and dynamical properties, while the LI was used to monitor the persistence and detection of intra-cloud and cloud-to-ground lighting flashes. Following this, the distribution of density flash groupings and the extent of each flash was analyzed in conjunction with previous analysis in order to infer convective intensity and cell organization. To combine the multi-platform datasets, the ESSL Weather Displayer was used to simultaneously visualize satellite, radar, and model-derived fields.This study shows that the integration of MTG data into operational meteorology provides substantial benefits for aviation operation, particularly at major hubs like Istanbul Airport where weather-related disruptions can have significant impacts.

The devastating fire events occurring during the intense fire season of 2023 have shown the importance of developing efficient fire detection methods capable of supporting the fire management activities. An enhanced configuration of the Normalized Hotspot Indices (NHI) algorithm has been developed in this direction to improve the fire mapping by satellite through near infrared (NIR) and short-wave infrared (SWIR) data (up to 20 m spatial resolution) from the Operational Land Imager (OLI/OLI2) and the Multispectral Instrument (MSI) aboard Landsat-8/9 (L8/9) and Sentinel-2 (S2) satellites, respectively. In this work, we show the results achieved by investigating the fire events occurring in California, Hawaii islands (USA), Yellowknife (Canada), Tenerife islands (Spain), Greece and Australia also through comparison with information from operational Landsat Fire and Thermal Anomaly (LFTA) product. Results of an extended validation analysis performed using information from well-established databases show that the enhanced NHI algorithm configuration enabled an accurate mapping of fire fronts with a very number of omission and commission errors. Moreover, the algorithm flagged up to 99% of fire pixels from the LFTA product over California and detected up to 70% of additional fire pixels, in night-time conditions, which better detailed the fire fronts and provided unique information about small-fire outbreaks. The effective integration of S2 (daytime) and L8/9 (daytime/night-time) observations, demonstrates that the enhanced NHI algorithm configuration may be used with success to analyse the dynamic evolution of flaming fronts by assessing/complementing information from satellite products at high-temporal/low-spatial resolution. The next implementation of the algorithm on from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellite and the Flexible Combined Imager (FCI) of the Meteosat Third Generation (MTG) opens some interesting perspectives also regarding its usage for the near-real time monitoring of wildfires