EUMETSAT Satellite Application Facility on Climate Monitoring
Data Set Description
CM SAF Surface Radiation MVIRI Data Set
DOI:10.5676/EUM_SAF_CM/RAD_MVIRI/V001
Reference Number:
Issue/Revision Index:
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DataSet/Desc/CMSAF/RAD/MVIRI
1.0
14.04.2014
Data Set Description
CM SAF Surface Radiation MVIRI Data Set
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Intent of the Document
This document summarizes essential information needed for users of any level who wish to use the CM SAF
Surface Radiation MVIRI Data Set for climate applications. The CM SAF Surface Radiation MVIRI Data
Set is a satellite-based climatology of the surface irradiance, the surface direct irradiance and the effective
cloud albedo derived from satellite-observations from the visible channel of the MVIRI instruments onboard
the geostationary Meteosat satellites.
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Point of Contact
EUMETSAT’s CM SAF: www.cmsaf.eu, email: [email protected]
3.
Data Field Description
The MVIRI instrument onboard the Meteosat First Generation satellites is a passive imaging radiometer with
three spectral channels: a visible channel covering 500-900 nm, and infra-red channels covering 5.7-7.1
microns and 10.5-12.5 microns. MVIRI comes with a spatial resolution of 2.5km for the visible and 5km for
the IR channels, sub-satellite point respectively. The Meteosat processing provides climate data sets of
effective cloud albedo, solar surface irradiance and direct irradiance. The applied method, i.e., MAGICSOL
described in detail in the Algorithm Theoretical Baseline document (SAF/CM/DWD/ATBD/MVIRI_HEL),
provides also information on the clear sky reflection which can be used to derive the surface albedo and the
surface solar net budget. These records enable the calculation of the surface shortwave net radiation budget.
The effective cloud albedo, the solar surface irradiance and the direct irradiance are available on a regular
0.03x0.03 degree grid. The spatial coverage covers the Meteosat disk up to a scanning angle of 70° (Figure
1). The data sets are available as hourly, daily and monthly means. All MAGICSOL data sets are introduced
in Table 1 with associated acronyms and units.
Figure 1: Area coverage for CM SAF Meteosat climate data sets, illustrated here for SIS.
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Data Set Description
CM SAF Surface Radiation MVIRI Data Set
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Table 1: Overview of MVIRI based data sets retrieved with MAGICSOL.
Acronym
SIS
CAL
SID
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Product title
Surface Incoming Shortwave Irradiance
Effective Cloud Albedo
Direct Irradiance at surface
Unit
W m-2
Dimensionless
W m-²
Data Origin
The processing of the MVIRI data is done in satellite projection. The results are transferred to the regular
latitude-longitude-grid using climate data operators (cdo, https://code.zmaw.de/projects/cdo). For the
retrieval of the effective cloud albedo, the Heliosat algorithm is used (Hammer et al., 2003). The original
version of the Heliosat method has been modified to generate a data set that meets climate quality. The
effective cloud albedo derived with the modified Heliosat version is used in combination with the clear-sky
surface radiation model MAGIC (Mueller et al., 2009) to derive the surface radiation products from the
geostationary Meteosat satellites number 2 to 7. The complete model (cloud and clear sky) is called
MAGICSOL and described in more detail in the CM-SAF ATBD (SAF/CM/DWD/ATBD/MVIRI_HEL).
The Heliosat method does not require calibrated radiances as input, but is directly based on image counts. To
consider the aging of the satellite instruments and the transitions between the satellites of the Meteosat series
a self-calibration method has been developed and applied. The self-calibration method overcomes the need
for well calibrated radiances, which are not available for Meteosat First Generation. The MAGICSOL
algorithm uses the satellite image information in order to retrieve the effective cloud albedo. From the
Heliosat algorithm the effective cloud albedo is derived. Together with information about the atmospheric
clear sky state (water vapour, aerosols, ozone) the effective cloud albedo is used as input for the MAGIC
method to calculate the direct irradiance and the solar surface irradiance.
5.
Validation and Uncertainty Estimate
The solar irradiance (SIS = Surface Incoming Solar radiation) and the direct irradiance (SID = Surface
Incoming Direct radiation) derived from the Meteosat first generation satellites (Meteosat 2 to 7, 1982-2005)
have been validated using ground based observations from the Baseline Surface Radiation Network (BSRN)
as a reference. The validation target values for the mean absolute difference between satellite-derived and
surface-measured radiation is defined by the target accuracy for monthly/daily means of 10/20 W/m² for SIS
and 15/25 W/m² for SID plus an uncertainty of the ground based measurements of 5 W/m².
The mean absolute differences of the monthly mean surface incoming solar (SIS) and surface incoming
direct radiation (SID) are 7.8 W/m2 and 11.0 W/m2, respectively, i.e., well below the respective targets of 15
and 25 W/m² for all sites. Moreover, nearly 90 % and about 85 % of the monthly mean absolute difference
values of surface solar and direct irradiance are below the target values.
The daily mean data of the surface incoming solar radiation (global irradiance) have a mean absolute
difference of 15 W/m², which is below the target value of 20 W/m². The mean absolute difference of the
daily mean direct irradiance (SID) is 21 W/m2, i.e. smaller than the target value of 30 W/m².
6.
Considerations for climate applications
The target accuracy is achieved for monthly and daily means. No trends in the bias are detectable,
demonstrating the stability and homogeneity of the surface incoming solar radiation and the surface
incoming direct radiation products. For the effective cloud albedo the accuracy is derived from the SIS
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CM SAF Surface Radiation MVIRI Data Set
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accuracy. The target value of 0.1 is reached with exception of the winter period for latitudes above 55 degree,
where higher uncertainties might occur. Low monthly/daily mean clear sky irradiance (<70/100 W/m²)
usually occur during wintertime above a latitude of +/-55°. The target accuracy might not be reached for
these regions and period. More over, for slant geometries (border of Heliosat coverage) it is expected that the
target accuracy is not met and even higher uncertainties might occur. Higher uncertainties might also occur
for snow covered regions.
In general for SIS, SID and CAL higher uncertainties are expected over regions with long lasting snow cover
and desert regions with (bright) sand surface. For SID, higher uncertainties are also expected in regions with
high variation in aerosol properties.
7.
Research Applications and
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Decision Support Applications
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Polytechnic University of Bucharest, Bucharest, Romania
ETH Zurich, Institute of Environmental Engineering
Institut für Atmosphäre und Umwelt, Goethe-Universität, Frankfurt am Main, Germany
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
UCAR Climate Data Guide
PV-GIS, Joint Research Centre, Ispra, Italy
Meteonorm, Meteotest, Bern, Switzerland
ARPA Lombardia Weather Service, Milan, Italy
Solar Radiation Atlas of Spain, Spanish Meteorological Agency, Madrid, Spain
EU MARS Crop Yield Forecasting System, Joint Research Centre, Ispra, Italy
Solar Resource Assessment in Benelux, Royal Meteorological Institute of Belgium, Brussels,
Belgium
Climate Suitability Maps for Agriculture, Agroscope, Zürich, Switzerland
Instrument Overview
The MVIRI instrument onboard the Meteosat First Generation satellites is a passive imaging radiometer with
three spectral channels: a visible channel covering 500-900 nm, and infra-red channels covering 5.7-7.1
microns and 10.5-12.5 microns. MVIRI comes with a spatial resolution of 2.5km for the visible and 5km for
the IR channels, sub-satellite point respectively. The second generation of Meteosat satellites is equipped
with the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) and the Geostationary Earth Radiation
Budget (GERB) instrument. However, retrieval algorithms that have been developed in order to use the
additional information gained by the improved spectral information of MSG can not be applied to the MVIRI
instrument, as they use spectral information that is not provided by MFG (NWC SAF cloud algorithm, CMSAF radiation algorithm). Hence, in order to be able to provide a long time series covering more than 20
years there is a need for a specific climate algorithm that can be applied to the satellites from the Meteosat
First and Second Generation. Moreover, the retrieved climate variable must have climate quality. The
MAGICSOL method in combination with the gnu-public license version of MAGIC does meet the above
mentioned requirements.
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Data Set Description
CM SAF Surface Radiation MVIRI Data Set
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References
Hammer, A., Heinemann, D., Hoyer, C., Kuhlemann, R., Lorenz, E., Müller, R., and Beyer, H. G.: Solar
energy assessment using remote sensing technologies, Remote Sens. Environ., 86, 423--432, 2003.
Mueller, R. W., C. Matsoukas, A. Gratzki, H.D. Behr, R. Hollmann, 2009: The CM SAF operational scheme
for the satellite based retrieval of solar surface irradiance – A LUT based eigenvector approach,
Remote Sens. Environ., 113, 1012-1024.
R. Posselt, R.W. Mueller, R. Stöckli, J. Trentmann, Remote sensing of solar surface radiation for climate
monitoring — the CM-SAF retrieval in international comparison, Remote Sensing of Environment,
Volume
118,
15
March
2012,
Pages
186-198,
ISSN
0034-4257,
http://dx.doi.org/10.1016/j.rse.2011.11.016.
CM-SAF documentation reports
(to be found under http://dx.doi.org/10.5676/EUM_SAF_CM/RAD_MVIRI/V001):
SAF/CM/DWD/ATBD/MVIRI_HEL/1.2: Algorithm Theoretical Basis Document (ATBD) - Meteosat
(MVIRI) Solar Solar Irradiance and effective Cloud Albedo Climate Data Sets.
SAF/CM/DWD/PUM/MVIRI_HEL/1.4: Product User Manual (PUM) - Meteosat (MVIRI) Solar Surface
Irradiance and effective Cloud Albedo Climate Data Sets.
SAF/CM/DWD/VAL/MVIRI_HEL/1.1: Validation Report - Meteosat (MVIRI) Solar Surface Irradiance and
effective Cloud Albedo Climate Data Sets.
11.
Revision History
10-01-2014 - Version 1 – Initial draft created by Rainer Hollmann.
16-01-2014 – Version 1 – Update of initial draft by Oliver Sus.
16-01-2014 – Version 1 – Inclusion of Applications by Reto Stöckli
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