Algorithm overview

The cloud, radiation and precipitation properties are retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of Meteosat Second Generation (MSG), operated by EUMETSAT. The MSG-CPP algorithm consists of several parts. The first part is the identification of cloudy pixels and determination of the cloud-top height and temperature. This is done using the GEO v2018 algorithms developed by the NWC SAF. In the second part the cloud optical and microphysical properties (cloud thermodynamic phase, cloud optical thickness and cloud particle effective radius, as well as cloud liquid/ice water path) are derived using algorithms developed in the CM SAF (Benas et al., 2017; Roebeling et al., 2006). The retrieval of these properties (except cloud phase) relies on observations of solar backscattered radiation, and is thus limited to daytime (solar zenith angle smaller than 84 degrees). The third part is the derivation of surface solar irradiance (total and direct/diffuse components) using the methods described in Greuell et al. (2013). Finally, precipitation intensity is estimated in two ways: (1) based on the retrieved cloud properties during daytime (Roebeling and Holleman, 2009), and (2) based on statistical relationships with the observed infrared brightness temperatures (Brasjen et al., 2015). The retrieval products cover the full MSG disk, including Europe, Africa, and the Atlantic Ocean.

Changes in March 2021 (version 2)

The MSG-CPP service was started in 2012. In March 2021 a major upgrade was implemented, including the following changes and improvements:

  • Cloud mask and height/temperature based on NWC SAF software: these are also derived during nighttime and give amongst others a better distinction between snow/ice and clouds
  • Improved cloud phase algorithm, utilizing multiple infrared channels (also available during nighttime)
  • Time-dependent calibration coefficients
  • Use of ECMWF/CAMS forecasted temperature, humidity, ozone, aerosols, snow cover, etc. instead of climatologies
  • Use of an updated surface albedo climatology based on MODIS C6 data
  • Extension of the viewing and solar zenith angles for daytime products from 78 to 84 degrees
  • A new set of radiative transfer look-up tables, both for clouds and surface irradiance, with modified cloud water and ice scattering properties
  • Updated parameterization for direct (horizontal) surface irradiance
  • A new infrared-based (day and night) precipitation product


Over the years, a large number of people have contributed to developing the MSG-CPP algorithms at KNMI. These include Rob Roebeling, Erwin Wolters, Hartwig Deneke, Wouter Greuell, Noud Brasjen, Gerd-Jan van Zadelhoff, Ping Wang, Nikos Benas, Piet Stammes, and Jan Fokke Meirink. Robert van Versendaal is responsible for the technical implementation of the processing chain. We thank Stijn Nevens (RMIB) for providing a C library for reading SEVIRI level 1b HRIT files. Maarten Plieger and John van de Vegte are thanked for setting up the ADAGUC server. EUMETSAT is acknowledged for generating and distributing the SEVIRI measurements, as well as for facilitating the development of the CPP algorithms through the CM SAF. The NWC SAF is acknowledged for providing the software for the retrieval of cloud mask and cloud-top height and temperature.

The data are free to use. It is appreciated if reference can be made to the KNMI MSG-CPP service, preferably by citing appropriate references included below, for any publications based on these data.

For questions about the MSG-CPP server and products, please send an email to Jan Fokke Meirink (


Benas, N., Finkensieper, S., Stengel, M., van Zadelhoff, G.-J., Hanschmann, T., Hollmann, R., and Meirink, J. F., 2017: The MSG-SEVIRI-based cloud property data record CLAAS-2, Earth System Science Data, 9, 415–434, doi:10.5194/essd-9-415-2017.

Brasjen, N. and J.F. Meirink, 2015: Precipitation estimation from MSG-SEVIRI infrared satellite imagery, in Proc. Meteorol. Satellite Conf. EUMETSAT, Toulouse, France, Sep. 2015, pp. 21–25.

Deneke, H.M., A. J. Feijt, and R. A. Roebeling, 2008: Estimating Global Irradiance from METEOSAT SEVIRI-derived Cloud Properties, Remote Sens. Environ., 112 (6), 3131-3141.

Greuell W., J. F. Meirink, and P. Wang, 2013: Retrieval and validation of global, direct, and diffuse irradiance derived from SEVIRI satellite observations, J. Geophys. Res. Atmos., 118, 2340–2361, doi:10.1002/jgrd.50194.

Meirink, J. F., et al., 2016: Algorithm Theoretical Basis Document, SEVIRI Cloud Physical Products, CLAAS Edition 2, EUMETSAT Satellite Application Facility on Climate Monitoring, SAF/CM/KNMI/ATBD/SEVIRI/CPP, Issue 2, Rev. 2, doi: 10.5676/EUM_SAF_CM/CLAAS/V002, 10 June 2016.

Meirink, J. F., de Vries, H., Knap, W., and Stammes, P., 2019: Globale straling meten met satellieten – terugblik op het zonnige jaar 2018, Meteorologica, 28, 12–15.

Meirink, J.F., R.A. Roebeling and P. Stammes, 2013: Inter-calibration of polar imager solar channels using SEVIRI, Atm. Meas. Tech., 6, 2495-2508, doi:10.5194/amt-6-2495-2013.

Roebeling, R. A., A. J. Feijt, and P. Stammes, 2006: Cloud property retrievals for climate monitoring: implications of differences between SEVIRI on METEOSAT-8 and AVHRR on NOAA-17, J. Geophys. Res., 111, D20210, doi:10.1029/2005JD006990.

Roebeling, R. A., and I. Holleman, 2009: SEVIRI rainfall retrieval and validation using weather radar observations, J. Geophys. Res., 114, D21202, doi:10.1029/2009JD012102.