The flexible combined imager onboard MTG: from design to calibration

Durand, Yannig; Hallibert, Pascal; Wilson, Mark; Lekouara, Mounir; Grabarnik, S.; Aminou, Donny; Blythe, Paul; Napierala, B.; Canaud, J. L.; Pigouche, Olivier; Ouaknine, Julien; Verez, Bernard

doi:10.1117/12.2196644

Cited by 18 publications

(10 citation statements)

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“…In contrast to LEO, GEO satellites provide many images each day over a fixed geographic region. New-generation GEO sensors include: the Advanced Baseline Imager (ABI; Schmit et al, 2005Schmit et al, , 2017 onboard NOAA GOES-16 and -17 (launched in November 2016 and March 2017, respectively, and designated as NOAA operational GOES-East and -West satellites in December 2018 and February 2019, respectively); the Advanced Himawari Imager (AHI, a twin to ABI; Bessho et al, 2016), onboard Himawari-8 and -9 (launched in October 2014 and November 2016, respectively); the Advanced Meteorological Imager (AMI, another ABI twin; Choi and Ho, 2015) onboard Geo-Kompsat-2A (launched December 2018; Kim et al, 2015); the Advanced Geosynchronous Radiation Imager (AGRI; Yang J. et al, 2017) onboard the FY-4 series (FY-4A, launched in November 2016, will be followed by FY-4B & C in 2019 and 2021, respectively); and the Flexible Combined Imager (FCI; Durand et al, 2015) to be launched on EUMETSAT Meteosat Third Generation Imaging satellites (MTG-I) beginning in 2021.…”

Section: Geostationary-orbit Infrared Sst Capabilitymentioning

confidence: 99%

Observational Needs of Sea Surface Temperature

et al. 2019

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Sea surface temperature (SST) is a fundamental physical variable for understanding, quantifying and predicting complex interactions between the ocean and the atmosphere. Such processes determine how heat from the sun is redistributed across the global oceans, directly impacting large-and small-scale weather and climate patterns. The provision of daily maps of global SST for operational systems, climate modeling and the broader scientific community is now a mature and sustained service coordinated by the Group for High Resolution Sea Surface Temperature (GHRSST) and the CEOS SST Virtual Constellation (CEOS SST-VC). Data streams are shared, indexed, processed, quality controlled, analyzed, and documented within a Regional/Global Task Sharing (R/GTS) framework, which is implemented internationally in a distributed manner. Products rely on a combination of low-Earth orbit infrared and microwave satellite imagery, geostationary orbit infrared satellite imagery, and in situ data from moored and drifting buoys, Argo floats, and a suite of independent, fully characterized and traceable in situ measurements for product validation (Fiducial Reference Measurements, FRM). Research and development continues to tackle problems such as instrument calibration, algorithm development, diurnal variability, derivation of high-quality skin and depth temperatures, and areas of specific interest such as the high latitudes and coastal areas. In this white paper, we review progress versus the challenges we set out 10 years ago in a previous paper, highlight remaining and new research and development challenges for the next 10 years

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Section: Geostationary-orbit Infrared Sst Capabilitymentioning

confidence: 99%

Observational Needs of Sea Surface Temperature

et al. 2019

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“…Other infrared imagers such as the Advanced Baseline Imager on the Geostationary Operational Environmental Satellites (Schmit et al, ) and the Advanced Himawari Imager (Bessho et al, ) would allow an extension of such analyses to other regions. Just like the Flexible Combined Imager on board the future Meteosat Third Generation (Durand et al, ), they provide information at a higher spatial resolution of 2 km, which could further improve the accurate localization of convective cores within MCSs.…”

Section: Discussionmentioning

confidence: 99%

Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection From Infrared Imagery

2018

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Mesoscale convective systems (MCSs) are frequently associated with rainfall extremes and are expected to further intensify under global warming. However, despite the significant impact of such extreme events, the dominant processes favoring their occurrence are still under debate. Meteosat geostationary satellites provide unique long‐term subhourly records of cloud top temperatures, allowing to track changes in MCS structures that could be linked to rainfall intensification. Focusing on West Africa, we show that Meteosat cloud top temperatures are a useful proxy for rainfall intensities, as derived from snapshots from the Tropical Rainfall Measuring Mission 2A25 product: MCSs larger than 15,000 km2 at a temperature threshold of −40°C are found to produce 91% of all extreme rainfall occurrences in the study region, with 80% of the storms producing extreme rain when their minimum temperature drops below −80°C. Furthermore, we present a new method based on 2‐D continuous wavelet transform to explore the relationship between cloud top temperature and rainfall intensity for subcloud features at different length scales. The method shows great potential for separating convective and stratiform cloud parts when combining information on temperature and scale, improving the common approach of using a temperature threshold only. We find that below −80°C, every fifth pixel is associated with deep convection. This frequency is doubled when looking at subcloud features smaller than 35 km. Scale analysis of subcloud features can thus help to better exploit cloud top temperature data sets, which provide much more spatiotemporal detail of MCS characteristics than available rainfall data sets alone.

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“…As of 29 June 2020, it is located at 3.5°E as a back-up spacecraft [81,82]. Note that other current or future imagers aboard geostationary satellites have similar spectral channels, e.g., the Advanced Baseline Imager on GOES-R, the Advanced Himawari Imager on Himawari-8/9, the Advanced Meteorological Imager on GEO-KOMPSAT-2A, the Advanced Geosynchronous Radiation Imager on Fengyun-4A or the Flexible Combined Imager on the Meteosat Third Generation satellites [83][84][85][86]. Thus, the method described here can in principle be extended to those as well.…”

Section: Msg/sevirimentioning

confidence: 99%

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

et al. 2021

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Volcanic ash clouds are a threat to air traffic security and, thus, can have significant societal and financial impact. Therefore, the detection and monitoring of volcanic ash clouds to enhance the safety of air traffic is of central importance. This work presents the development of the new retrieval algorithm VACOS (Volcanic Ash Cloud properties Obtained from SEVIRI) which is based on artificial neural networks, the thermal channels of the geostationary sensor MSG/SEVIRI and auxiliary data from a numerical weather prediction model. It derives a pixel classification as well as cloud top height, effective particle radius and, indirectly, the mass column concentration of volcanic ash clouds during day and night. A large set of realistic one-dimensional radiative transfer calculations for typical atmospheric conditions with and without generic volcanic ash clouds is performed to create the training dataset. The atmospheric states are derived from ECMWF data to cover the typical diurnal, annual and interannual variability. The dependence of the surface emissivity on surface type and viewing zenith angle is considered. An extensive dataset of volcanic ash optical properties is used, derived for a wide range of microphysical properties and refractive indices of various petrological compositions, including different silica contents and glass-to-crystal ratios; this constitutes a major innovation of this retrieval. The resulting ash-free radiative transfer calculations at a specific time compare well with corresponding SEVIRI measurements, considering the individual pixel deviations as well as the overall brightness temperature distributions. Atmospheric gas profiles and sea surface emissivities are reproduced with a high agreement, whereas cloudy cases can show large deviations on a single pixel basis (with 95th percentiles of the absolute deviations > 30 K), mostly due to different cloud properties in model and reality. Land surfaces lead to large deviations for both the single pixel comparison (with median absolute deviations > 3 K) and more importantly the brightness temperature distributions, most likely due to imprecise skin temperatures. The new method enables volcanic ash-related scientific investigations as well as aviation security-related applications.

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The flexible combined imager onboard MTG: from design to calibration

Cited by 18 publications

References 0 publications

Observational Needs of Sea Surface Temperature

Observational Needs of Sea Surface Temperature

Wavelet Scale Analysis of Mesoscale Convective Systems for Detecting Deep Convection From Infrared Imagery

The New Volcanic Ash Satellite Retrieval VACOS Using MSG/SEVIRI and Artificial Neural Networks: 1. Development

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