An Introduction to Meteosat Second Generation (MSG)

Schmetz, Johannes; Pili, Paolo; Tjemkes, Stephen; Just, Dieter; Kerkmann, Jochen; Rota, Sergio; Ratier, Alain

doi:10.1175/1520-0477(2002)083<0977:aitmsg>2.3.co;2

Cited by 643 publications

(559 citation statements)

References 26 publications

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“…Since the CM SAF SEVIRI CFC is distributed for HH : 45 (scan starting time) and the scan by SEVIRI takes 12 min (Schmetz et al, 2002) and reaches the area over Hannover after approximately 10 min, the time HH : 00 was chosen for the CFC calculation by HSI and also SYNOP. Only values measured under the conditions of a solar zenith angle lower than 80 • were accepted, since HSI and SYNOP data are based on the visible spectrum of the solar radiation (camera and human eye).…”

Section: Seviri Data Processingmentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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Abstract. In this case study we compare cloud fractional cover measured by radiometers on polar satellites (AVHRR) and on one geostationary satellite (SEVIRI) to ground-based manual (SYNOP) and automated observations by a cloud camera (Hemispherical Sky Imager, HSI). These observations took place in Hannover, Germany, and in Lauder, New Zealand, over time frames of 3 and 2 months, respectively.Daily mean comparisons between satellite derivations and the ground-based HSI found the deviation to be 6 ± 14 % for AVHRR and 8 ± 16 % for SEVIRI, which can be considered satisfactory. AVHRR's instantaneous differences are smaller (2 ± 22 %) than instantaneous SEVIRI cloud fraction estimates (8 ± 29 %) when compared to HSI due to resolution and scenery effect issues. All spaceborne observations show a very good skill in detecting completely overcast skies (cloud cover ≥ 6 oktas) with probabilities between 92 and 94 % and false alarm rates between 21 and 29 % for AVHRR and SEVIRI in Hannover, Germany. In the case of a clear sky (cloud cover lower than 3 oktas) we find good skill with detection probabilities between 72 and 76 %. We find poor skill, however, whenever broken clouds occur (probability of detection is 32 % for AVHRR and 12 % for SEVIRI in Hannover, Germany).In order to better understand these discrepancies we analyze the influence of algorithm features on the satellite-based data. We find that the differences between SEVIRI and HSI cloud fractional cover (CFC) decrease (from a bias of 8 to almost 0 %) with decreasing number of spatially averaged pixels and decreasing index which determines the cloud coverage in each "cloud-contaminated" pixel of the binary map. We conclude that window size and index need to be adjusted in order to improve instantaneous SEVIRI and AVHRR estimates. Due to its automated operation and its spatial, temporal and spectral resolution, we recommend as well that more automated ground-based instruments in the form of cloud cameras should be installed as they cover larger areas of the sky than other automated ground-based instruments. These cameras could be an essential supplement to SYNOP observation as they cover the same spectral wavelengths as the human eye.

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Section: Seviri Data Processingmentioning

confidence: 99%

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

et al. 2015

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“…Therefore, for larger particles, the DT 8.7-10.8 increases. The water vapor absorption in the 8.7-lm channel is higher relative to the 10.8-lm channel (Soden and Bretherton 1996;Schmetz et al 2002). This is why DT 8.7-10.8 is lower for low optical thicknesses.…”

Section: Parameters For Identification Of Raining Pixelsmentioning

confidence: 94%

Rainfall detection over northern Algeria by combining MSG and TRMM data

Ouallouche

Ameur

2014

Appl Water Sci

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In this paper, a new method to delineate rain areas in northern Algeria is presented. The proposed approach is based on the blending of the geostationary meteosat second generation (MSG), infrared channel with the low-earth orbiting passive tropical rainfall measuring mission (TRMM). To model the system designed, we use an artificial neural network (ANN). We seek to define a relationships between three parameters calculated from TRMM microwave imager (TMI) associated with four parameters from infrared sensors of MSG satellite and two classes (rain, no-rain) from precipitation radar (PR) TRMM data. The seven spectral parameters issued from MSG and TMI are used as input data. Rain/no-rain classes from PR are used as the output data of this ANN. Two steps are necessary: training and validation. Results in the developed scheme are compared with the results of a reference method which is the scattering index (SI) method. The result shows that the developed model works very well and overcomes the shortcomings of the SI method.

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“…Both parameters are part of the CLAAS (CLoud property dAtAset Using SEVIRI) data set by CM SAF (Schulz et al, 2009) that includes cloud micro-and macrophysical properties as well as surface albedo and spans the time period 2004-2011. The radiances were measured with the passive optical imaging radiometer SEVIRI (Schmetz et al, 2002). It is equipped with 12 spectral channels at visible and infrared wavebands.…”

Section: Generation Of Lwp and Cty From Seviri Measurementsmentioning

confidence: 99%

Characteristics of cloud liquid water path from SEVIRI onboard the Meteosat Second Generation 2 satellite for several cloud types

et al. 2014

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Abstract. In this study the temporal and spatial characteristics of the liquid water path (LWP) of low, middle and high level clouds are analysed using space-based observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument onboard the Meteosat Second Generation 2 (MSG 2) satellite. Both geophysical quantities are part of the CLAAS (CLoud property dAtAset using SEVIRI) data set and are generated by EUMETSAT's Satellite Application Facility on Climate Monitoring (CM SAF). In this article we focus on the statistical properties of LWP, retrieved during daylight conditions, associated with individual cloud types. We analysed the intrinsic variability of LWP, that is, the variability in only cloudy regions and the variations driven by cloud amount. The relative amplitude of the intrinsic diurnal cycle exceeded the cloud amount driven amplitude in our analysed cases. Our results reveal that each cloud type possesses a characteristic intrinsic LWP distribution. These frequency distributions are constant with time in the entire SE-VIRI field of view, but vary for smaller regions like Central Europe. Generally the average LWP is higher over land than over sea; in the case of low clouds this amounts to 15-27 % in 2009. The variance of the frequency distributions is enhanced as well. Also, the average diurnal cycle of LWP is related to cloud type with the most pronounced relative diurnal variations being detected for low and middle level clouds. Maps of the relative amplitude and the local time of maximum LWP show the variation throughout the SEVIRI field of view.

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An Introduction to Meteosat Second Generation (MSG)

Cited by 643 publications

References 26 publications

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Comparing satellite- to ground-based automated and manual cloud coverage observations – a case study

Rainfall detection over northern Algeria by combining MSG and TRMM data

Characteristics of cloud liquid water path from SEVIRI onboard the Meteosat Second Generation 2 satellite for several cloud types

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