The Meteosat Third Generation (MTG) programme is intended to provide meteorological data from the geostationary orbit as a continuation of the Meteosat Second Generation (MSG) services at least until the mid-2040s. The programme consists of a twin satellite concept, based on three-axis stabilised platforms: four imaging satellites (MTG-I) and two sounding satellites (MTG-S). The first MTG satellite is currently scheduled for a launch around the fourth quarter of 2021. Images from the Flexible Combined Imager (FCI) instrument on board the MTG-I satellites will be used to derive Atmospheric Motion Vectors (AMVs) in a wide range of frequencies, from visible to infrared, ensuring the continuity of the Full Earth Scan service currently provided by Meteosat-11. AMVs are derived from satellites by tracking clouds or water-vapour features in consecutive satellite images. They are an important meteorological product because they are assimilated in Numerical Weather Prediction (NWP) models and contribute to the improvement of forecast scores. The MTG-FCI AMV extraction algorithm is largely based on that of MSG, with a few important differences (three images instead of four, no intermediate product averaging, etc.). This MTG-FCI AMV algorithm strategy is expected to provide more instantaneous information on the wind vectors, which is more suitable for the smaller-scale evolution of NWP models. In this paper, the current status of the MTG-FCI AMV prototype is presented, together with a detailed comparison of the MSG and MTG-FCI AMV algorithm performances using one month of MSG data. The impact of the new MTG-FCI strategy on the AMV product and its performance against forecast are also analysed.
This Paper provides an overview on the first results of the Metop-C satellite, third and last part of the series of three Metop-satellites of the EUMETSAT Polar System (EPS). EPS is the European contribution to the Polar Meteorological Satellite Observing System. It forms a part of the Initial Joint Polar System (IJPS), formed with NOAA (National Oceanic and Atmospheric Administration). The Metop-C satellite, launched on the 7 November 2018 from the Guyana Space Centre in Kourou, and is finalizing its commissioning activities. The Metop satellites were developed in co-operation with the European Space Agency (ESA). Seven meteorological instruments (among 10) are embarked on Metop-C satellites (eight on Metop-A and –B where the HIRS/4 instrument was embarked as well). These are the IASI (Infrared Atmospheric Sounding Interferometer), developed by CNES in co-operation with EUMETSAT, the AVHRR (Advanced Very High Resolution Radiometer) and AMSU-A (Advanced Microwave Sounding Unit-A) instruments, provided by NOAA, the Microwave Humidity Sounder (MHS), developed by EUMETSAT and the GRAS (GNSS (Global Navigation Satellite System) Receiver for Atmospheric Sounding) instrument, the GOME-2 (Global Ozone Monitoring .-2) instrument and ASCAT (Advanced Scatterometer), developed by ESA as part of the space segment. Metop instrument data – in particular the sounding instruments - provide an essential contribution to global operational Numerical Weather Prediction (NWP). Climate monitoring and atmospheric composition monitoring and ocean and cryosphere observations are further application areas supported by Metop instrument data. Results from the commissioning phase and first application impacts will be presented. After its successful commissioning, there will be three Metop-satellites in orbit for about three years.
The EPS-SG Visible/Infrared Imaging mission is dedicated to supporting the optical imagery user needs for Numerical Weather Prediction (NWP), Nowcasting (NWC) and climate in the 2020 onwards timeframe. The VII mission is fulfilled by the METimage instrument, to be embarked on the Metop-SG-A satellites. METimage will fly in the mid-morning orbit of the Joint Polar System, whilst the early-afternoon orbits are served by the JPSS (U.S. Joint Polar Satellite System) Visible Infrared Imager Radiometer Suite (VIIRS). METimage is a cross-purpose medium resolution, multispectral optical imager, measuring radiation emitted and reflected by the Earth from a low-altitude sun synchronous orbit with a minimum swath width of 2700 km. The top of the atmosphere outgoing radiance will be sampled every 500 m (at nadir) with measurements made in 20 spectral channels ranging from 443 nm in the visible up to 13.345 μm in the thermal infrared.
The following METimage geophysical products will be generated and validated by EUMETSAT:
Cloud mask
Atmospheric motion vectors.
Cloud top height, microphysics and volcanic ash
Cloud top pressure using the oxygen-A band
Total precipitable water vapour from METimage visible/near-infrared bands.
Total precipitable water vapour from METimage thermal infra-red bands
This paper focuses on the validation activities planned for the METimage geophysical products to ensure they meet user requirements for the lifetime of the mission. The validation of level 2 variables relies on the availability of simultaneous and independent data providing the same information on the same horizontal scale. Validation methods include intercomparisons with other validated missions through simultaneous nadir overpasses (both low Earth orbit and geostationary), comparisons with ground based observations, and short term weather forecasts. The level 2 product performance will be validated with respect to user requirements for all geographic regions and seasonal variation.
Several techniques exist to correct the estimation of the cloud top pressure for semi-transparency effect, and the advent of
Meteosat Second Generation (MSG) enables the simultaneous use of the IR/CO2 ratioing methodology in addition to the
IR/WV intercept method. This paper presents the performances of these two methods using simulated data. The FASDOM
radiative code has been used to simulate MSG radiances for various types of clouds at different levels in the troposphere,
using different atmospheric profiles. Performances of the methods are presented as function of several atmospheric and cloud
parameters.
Atmospheric Motion Vectors (AMVs) are one of the most important products generally derived from geostationary satellites,
and especially from Meteosat at EUMETSAT, because they constitute a very important part of the observational data fed to
Numerical Weather Prediction. The height estimation or 'assignment' (HA) is still the most challenging task in the AMV
extraction scheme. The advent of Meteosat Second Generation provides many new opportunities for improving the HA of
AMVs. Indeed, the existence of a CO2 absorption channel at 13.4 μm on the SEVIRI instrument enables the simultaneous use
of the IR/CO2 ratioing methodology in addition to the 'WV-IRW intercept method' (also called STC), for semi-transparent
cases. Due to the existence of several Water Vapour and Infrared channels on SEVIRI, each method is implemented in
slightly different configuration, and several pressures are then calculated for each AMV. It was expected at first to use the
agreement of these pressures as a quality check for the final AMV height. Unfortunately, the various methods (STC and CO2
slicing) have clearly their own sensitivity and domain of application, which makes a quality check very challenging. It
appeared then necessary to define these domains of application more precisely, in order that better use may be made of these
methods operationally.
This paper presents such results using simulated SEVIRI radiances calculated by the FASDOM radiative transfer code.
FASDOM accounts for gaseous absorption as well as cloud scattering and absorption and can precisely consider various types
of clouds with various microphysical properties. We then have the possibility to compare the outputs of the HA methods
knowing precisely the input to the model, especially the pressure of the simulated cloud.
Upper-level divergence is often associated to low-level convergence through the principle of mass continuity, inducing an ascending motion of the air mass. Vertical motion, either upward or downward, is recognized as an important parameter in the atmosphere because it affects the formation or dissipation of clouds. An important application of vertical motion observations is to use this quantity as diagnostic for the occurrence of rain. For instance, extensive regions of precipitation associated with extratropical cyclones are regions of large-scale upward motion. Similarly, the nearly cloud-free regions in large anticyclones are regions in which air is subsiding. Previous studies indicated that the upper tropospheric humidity (UTH) field is also governed by large scale dynamics, and is in a general good agreement with the patterns of high level wind divergence. That suggests that a divergence parameter could be very useful in the analysis for numerical weather prediction. Holmlund (2000a) has described the possibility to infer divergence fields from Atmospheric Motion Vectors (AMVs) that have been derived from tracking cloud and humidity features in the 6.2 μm WV channel of Meteosat. Such algorithm has been developed at EUMETSAT, and tested on Meteosat 8 data. This paper describes the calculation's process of the upper level divergence, and presents some results over large scale convective systems observed by Meteosat 8 over tropical areas.
Atmospheric Motion Vectors (AMVs) are one of the most important products generally derived from all geostationary satellites, and especially from Meteosat at EUMETSAT, because they constitute a very important part of the observation data fed to Numerical Weather Prediction. The resolution of the current operational products is 160 km at the sub-satellite point. The height assignment is currently the most challenging task in the AMV extraction scheme. The main approach used for Meteosat was the so-called 'WV-IRW intercept method' for semi-transparent cases. Opaque cloud heights are calculated from the representative Equivalent Black Body Temperatures derived from the AMV target area. The advent of Meteosat 8 provides many new opportunities for improve height assignment of AMVs. Existence of a CO2 absorption channel at 13.4 μm on SEVIRI instrument enables to use simultaneously the IR/CO2 ratioing methodology in addition to the semi-transparency technique. Due to the existence of several Water Vapour and Infrared channels on SEVIRI, each method can be implemented in slightly different configuration, and finally, there are 15 cloud top pressure schemes implemented in the MSG-MPEF. This paper presents a comparison of some of these methods using Meteosat 8 data.
The first of the new generation of Meteosat satellites, known as Meteosat Second Generation (MSG), was launched in August 2002. From its geostationary orbit, the satellite's radiometer, the spinning enhanced visible and infrared imager (SEVIRI), observes the full disk of the Earth with an unprecedented repeat cycle of 15 minutes in 12 spectral channels, having a sampling distance of three kilometres at nadir (1 km for the high resolution channel). For comparison, the first-generation Meteosat satellite covers only three spectral channels and has an imaging repeat cycle of 30 minutes, with a sampling distance between 2.5 and 5 km. MSG offers a wealth of new observational capabilities that could benefit weather forecasting and support severe weather warnings. Significant indirect benefits will come through improved weather forecasts that predict e.g. wind fields more accurately. With the beginning of MSG's operational phase on 29 January 2004, the satellite was renamed to Meteosat-8. The Meteosat-8 operational system also includes a suite of meteorological data which are extracted from the multi-channel image information, as e.g. winds, cloud analysis, atmospheric humidity and atmospheric instability over the entire field of view. This paper presents a general overview over the Meteosat-8 imagery and will especially focus on the meteorological parameters - including the underlying algorithms - that are extracted at EUMETSAT.
Aerosol remote sensing over land requires knowing the surface reflectance in some spectral bands. Dense dark vegetation can be used in the blue and in the red based on ground based measurements of their reflectances or even space measurements from a statistical analysis for clear days. An aerosol remote sensing algorithm based on DDV is available on MERIS data (Santer et al., 1999). An other alternative is to derive the surface reflectances from space as far as you have ground based characterization of the aerosols to perform suitable atmospheric correction, at least on a representative time series (Borde and Verdebout, 2001). The two algorithms, applied on SeaWiFS images, are compared over three sites (Toulouse, Ispra, Adriatic) for which ground based measurements are available.
The key problem in aerosol retrieval over land is to distinguish between surface and atmospheric contribution to the satellite reflectance. In principle a method similar to the classical Dense Dark Vegetation could be used over brighter surfaces if the surface BRDF could be described with sufficient accuracy. Studying a time series of data, taking into account geometrical conditions, and assuming the ground BRDF to be constant over several days, variations of the satellite signal may be mainly attributed to variations of the atmospheric optical properties. By fitting a subset of satellite observations associated with ground photometer data, the best-fit of BRDF parameters could be determined. Using then this surface characterization as an input of the inversion process, the aerosol optical thickness can in principle be retrieved routinely. Such method has been already explored with a time series of VEGETATION data for several Western European sites. The aerosol optical thickness retrieved from the satellite data and that derived from CIMEL measurements were in good agreement, even for cases of high optical depth. The method has been now improved with the sensor SeaWifS. Aerosol properties retrieved from SeaWifS data have been compared with those measured in-situ by sunphotometers.
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