What will Meteosat Third Generation Bring to the Table for Severe Storm Analysis?

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 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.

The Geostationary Operational Environmental Satellite-14 (GOES-14) imager was operated by the National Oceanic and Atmospheric Administration (NOAA) in an experimental rapid scan 1-min mode during parts of the summers of 2012 and 2013. This scan mode, known as the super rapid scan operations for GOES-R (SRSOR), emulates the high-temporal-resolution sampling of the mesoscale region scanning of the Advanced Baseline Imager (ABI) on the next-generation GOES-R series. This paper both introduces these unique datasets and highlights future satellite imager capabilities. Many phenomena were observed from GOES-14, including fog, clouds, severe storms, fires and smoke (including the California Rim Fire), and several tropical cyclones. In 2012 over 6 days of SRSOR data of Hurricane Sandy were acquired. In 2013, the first two days of SRSOR in June observed the propagation and evolution of a mid-Atlantic derecho. The data from August 2013 were unique in that the GOES imager operated in nearly continuous 1-min mode; prior to this time, the 1-min data were interrupted every 3 h for full disk scans. Used in a number of NOAA test beds and operational centers, including NOAA’s Storm Prediction Center (SPC), the Aviation Weather Center (AWC), the Ocean Prediction Center (OPC), and the National Hurricane Center (NHC), these experimental data prepare users for the next-generation imager, which will be able to routinely acquire mesoscale (1,000 km × 1,000 km) images every 30 s (or two separate locations every minute). Several animations are included, showcasing the rapid change of the many phenomena observed during SRSOR from the GOES-14 imager.

TheAdvanced Baseline Imager (ABI) onboard the new generation of National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellite (GOES) series provides 10 min, multi-spectral, 500 m to 2 km observations,with significantly improved capabilities compared to the previous GOES sensors. For the first time GOES data are available with sufficient resolution to potentially enable burned area mapping using the reflective wavelength data. In this study GOES-16 ABI unburned and burned reflectance acquired over four extensive burns in the United States and South America were examined. Significant diurnal variations in the ABI reflectance due to surface reflectance anisotropy and changes in the position of the sun at each ABI observation time were observed, with unburned near-infrared (NIR) reflectance that varied by more than 50% over the day, and this variation was greater than the change in reflectance due to fire. The diurnal reflectance variation included locally increased backscatter reflectance acquired under near hot-spot conditions when the solar zenith was close to the view zenith. The spectral and diurnal temporal suitability of the ABI data for burned area mapping was assessed for the red, NIR and short wave infrared (SWIR) bands using the transformed divergence metric applied to the unburned and burned reflectance values for each ABI acquisition time. The separabilities varied diurnally but with no consistent view-solar geometry or time when the separabilities were consistently high among the four sites. The paper results demonstrate that use of ABI reflectance without consideration of, or correction, for surface anisotropy will be unreliable. The most suitable ABI bands to detect burned areas are the NIR, which provided high diurnal separabilities except in the early morning and late evening, and the normalized burn ratio (NBR), computed as the difference between the NIR and SWIR reflectance divided by their sum, that had more consistently high diurnal separability. Implications and recommendations for future ABI burned area mapping research are discussed.

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.

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.

The GOES-R series is the latest in a long line of American geostationary weather satellites operated by the National Oceanic and Atmospheric Administration (NOAA). The two Geostationary Lightning Mapper (GLM) instruments currently operating on the GOES-16 and GOES-17 satellites give NOAA a unique new capability to map in-cloud and cloud-to-ground lightning flashes across the entire hemisphere within seconds of their occurrence. GLM enables improved warning times for severe weather events, decreased false alarms, persistent coverage over wide geographical areas without sampling bias, and long-term monitoring of trends linked to the changing climate. Viewed from space, emissions from lightning appear as a series of brief (~500 μs) optical pulses diffused through clouds over scales of tens to thousands of km2. A significant portion of the emitted optical radiation is in the form of emission lines, including a prominent neutral atomic oxygen triplet whose emission lines are near 777 nm. GLM discriminates lightning flashes from the bright sunlit cloud background by taking advantage of the spatial, temporal, and spectral characteristics of the optical signature of lightning. This paper describes key design drivers in the development of GLM, methods used to calibrate the instrument, and lessons learned from on-orbit testing. We discuss optimization of the entire signal chain, from the telescope optics to the ground processing algorithms.

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

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).

Marine Mammal ScienceVolume 29, Issue 2 p. E193-E202 Notes An investigation of sound use and behavior in a killer whale (Orcinus orca) population to inform passive acoustic monitoring studies Marla M. Holt, Corresponding Author Marla M. Holt Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ACorresponding author (e-mail: marla.holt@noaa.gov).Search for more papers by this authorDawn P. Noren, Dawn P. Noren Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ASearch for more papers by this authorCandice K. Emmons, Candice K. Emmons Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ASearch for more papers by this author Marla M. Holt, Corresponding Author Marla M. Holt Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ACorresponding author (e-mail: marla.holt@noaa.gov).Search for more papers by this authorDawn P. Noren, Dawn P. Noren Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ASearch for more papers by this authorCandice K. Emmons, Candice K. Emmons Marine Mammal & Seabird Ecology Team, Conservation Biology Division, Northwest Fisheries Science Center, National Marine Fisheries Service (NMFS), National Oceanic and Atmospheric Administration (NOAA), 2725 Montlake Boulevard East, Seattle, Washington, 98112 U.S.ASearch for more papers by this author First published: 12 September 2012 https://doi.org/10.1111/j.1748-7692.2012.00599.xCitations: 13Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume29, Issue2April 2013Pages E193-E202 RelatedInformation

ESA and EUMETSAT have initiated joint preparatory activities for the formulation and definition of the Meteosat Third Generation (MTG) geostationary system to ensure the continuity and improvement of the Meteosat Second Generation (MSG) system. The MTG will become the new system to be the backbone of the European operational meteorological services from 2015, in particular, will ensure the continuation of the imagery missions. The first phases were devoted to the definition and consolidation of end user requirements and priorities in the field of Nowcasting and Very Short Term Weather Forecasting (NWC), Medium/Short Range global and regional Numerical Weather Prediction (NWP), Climate and Air Composition Monitoring and to the definition of the relevant observation techniques. The following missions have been analysed and preliminary concepts studied: The Flexible Combined Imager, an improvement of the actual MSG-SEVIRI Imager Lightning Imagery Mission IR Sounding Mission UV-VIS-NIR Sounding Mission as a payload complement from GMES. After pre-phase A mission studies (2003-2006), where preliminary instrument concepts were investigated allowing in the same time to consolidate the technical requirements for the overall system study, a phase A study on MTG has been launched at the beginning of February 2007 for the space segment system feasibility and programmatic aspects to be accomplished during 2007-2008 time frame. The space segment phase A study will cover all elements to the level of details allowing to conclude on the feasibility of the system and to produce cost estimates with a good level of confidence. This paper provides an overview of the outcome of the MTG space segment at the end of phase A, addressing the progress accomplished for the various payloads in terms of achievable performances including Radiometry and Image Navigation and Registration aspects. It namely focuses onto the Imaging and IR Sounding, Lightning Missions, introduces the UV-VIS-NIR Sounding mission concept status, establishes the critical technologies and introduces the way forward to the implementation of the MTG development programme.

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.

ESSL and EUMETSAT have organised several expert workshops on using and visualizing the Lightning Imager (LI) data from the Meteosat Third Generation satellite. Also based on the recommendations gained from the workshop, ESSL developed the LI product visualizations presented to the forecasters at the ESSL-EUMETSAT forecaster training Testbeds in 2024 and 2025. These involved point- and grid-based data, such as the size of observed flashes, group density calculated over small grids, or the spatial extent of the observed flashes. Participants of each of the Testbed weeks were asked to evaluate the products and fill out questionnaires. We present the workshops' main results and the summary of the evaluation provided by the Testbed participants. We concentrate on two issues: how useful the LI data is for nowcasting severe convective storms and which products or visualizations were preferred by the forecasters.Besides that, the ESSL staff has been inspecting the LI data over both convective seasons. Using the experience from 2024, ESSL has produced a guide on using the LI data alongside a detailed description of the LI behaviour for 12 cases of severe and non-severe convective storms. Patterns in LI data were compared with storm hazards on the ground reported in the ESWD. We present some insights gained from the data usage during these two years. This includes some interesting patterns or artifacts, such as a few cases of severe storms showing a sudden dip in the lightning, the occurrence of a lightning ring surrounding the updraft area, or a large fraction of flashes detected from the side of the storm as a reflection from the low clouds.

The National Ocean Service, which I've headed since December 1983, is one of the major line components of the National Oceanic and Atmospheric Administration (NOAA). NOAA, in turn, is part of the Department of Commerce and is the leading federal agency in the oceanic and atmospheric sciences. Other agencies are involved in the earth sciences, such as the Department of the Interior's Geological Survey, or are in the business of environmental regulations, like the U.S. Environmental Protection Agency, but NOAA is the one federal agency charged specifically with analyzing and predicting oceanic and atmospheric components of the earth's environment as a whole. The importance of this global, integrated air‐sea approach is reflected in the five NOAA line offices.This past December, NOAA line offices were reorganized to consolidate programs as part of the Reagan Administration's general government‐wide belt tightening (see Figure 1). The idea was for NOAA to grow leaner but stronger. The main thrust of the work of the Weather Service and the Marine Fisheries Service remained the same. The Office of Oceanic and Atmospheric Research continued to provide research support to the other NOAA components. A trimmed down Environmental Data and Information Service merged with the National Environmental Satellite Service to become today's National Environmental Satellite, Data, and Information Service. Also, this past December the NOAA Office of Coastal Zone Management joined forces with the National Ocean Survey to become the National Ocean Service.

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.