MSGCPP product description

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MSG Cloud Physical Properties (CPP) product description


Contents

Identification

Product description

Abstract

The cloud, radiation and precipitation properties are retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of Meteosat Second Generation (MSG) with the Cloud Physical Properties (CPP) algorithm of KNMI (Roebeling et al., 2006). The MSG-CPP algorithm consists of three steps. The first step is to separate cloud free from cloud contaminated and cloud filled pixels, which is done with a modified version of the cloud detection algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) (J. Riédi, University of Lille). In the second step the primary cloud properties (Cloud Top Temperature, Cloud Phase, Cloud Optical Thickness and Cloud Particle size) are retrieved in an iterative manner by simultaneously comparing satellite observed reflectances at visible (0.6 um) and near-infrared (1.6 um) wavelengths to Look Up Tables (LUTs) of simulated reflectances of water and ice clouds for given optical thicknesses, particle sizes and surface albedos. These LUTs have been generated with the Doubling Adding KNMI (DAK) radiative transfer model (Stammes, 2001). The third step is to calculate the secondary cloud properties (Cloud Water Path, Cloud Droplet Number Concentration, Cloud Geometrical Thickness, Surface Solar Irradiance, Precipitation occurrence and intensity) for which the retrieval largely relies on the primary cloud properties. The retrievals are limited to satellite and solar viewing zenith angles smaller than 78°.

Purpose

Monitoring clouds, radiation and precipitation properties.


Application

High quality information on global distributions of cloud, radiation and precipitation properties is needed to improve our understanding of the role of these properties in weather and climate systems and for nowcasting. Observations from meteorological satellites are well suited to capture the large spatial and temporal variations that are typical for these properties. Most meteorological satellite instruments are passive imagers, i.e., radiometers that measure reflected scattered and emitted radiation from the Earth's surface, atmosphere and clouds. The inversion procedures that are necessary to convert these measured radiances into cloud, radiation and precipitation properties comprise of a cloud detection scheme and a retrieval scheme that uses radiative transfer simulations.

Time period of content

Time period of content

1 January 2004 – date.


Currentness reference

Actual time of observation.


Status

Progress

Complete and validated.


Maintenance and update frequency

Irregularly.


Spatial Domain

Bounding coordinates

MSG full disk: Longitude [-50, 50], latitude [-80,80]


Keywords

Theme

Atmosphere, meteorology, hydrology.


Place

MSG full disk.


Stratum

Troposphere.


Temporal

Recent.


Access constraints

None.


Use constraints

None.


Point of contact

Jan Fokke Meirink, KNMI, meirink_at_knmi.nl


Citation

Originator

KNMI.


Publication date

1 December 2011


Title

MSG Cloud Physical Properties (CPP) algorithm.


Edition

Version 4.0


Preview

Msgcpp dcld-thickness of liquid water cloud.png Msgcpp surface downwelling shortwave flux.png

Figure 1. “Snapshot” Cloud Optical Thickness and Solar Surface Irradiance on 1 December 2011 at 12:00 UTC as retrieved with the CPP algorithm from SEVIRI full disk observations.

Unique identifiers

Source data unique identifier:

iso_dataset:uid = "fc7bf40c-fb89-49cf-bf93-eaa93aff9bab"

Metadata identifier:

iso_dataset:metadata_id = "bcf1b36d-3207-4a88-9d36-84728b2afdc6"

Data set credit

The development of the CPP algorithm was done by Hartwig Deneke, Jan Fokke Meirink, Rob Roebeling, and Erwin Wolters (KNMI). We are grateful to Stijn Nevens (RMIB) for providing a C library for reading SEVIRI level 1b HRIT files. EUMETSAT is acknowledged for generating and distributing the SEVIRI measurements, as well as for facilitating the development of the CPP algorithm through the CM-SAF.

Cross reference

Literature

Bennartz, R., P. Watts, J.F. Meirink and R.A. Roebeling, 2010: Rainwater path in warm clouds derived from combined visible/near-infrared and microwave satellite observations, J. Geophys. Res., 2010, 115, D19120, doi:10.1029/2009JD013679.

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.

Deneke, H.M., W.H. Knap and C. Simmer, 2009: Multiresolution Analysis of the Temporal Variance and Correlation of Transmittance and Reflectance of an Atmospheric Column, J. Geophys. Res., 2009, doi:10.1029/2009JD011978.

Deneke, H.M. and R.A. Roebeling, 2010: Downscaling of METEOSAT SEVIRI 0.6 and 0.8 micron channel radiances utilizing the high-resolution visible channel, Atm. Chem. Phys., 10, 9761-9772.

Greuell W. and R. A. Roebeling, 2009: Towards a standard procedure for validation of satellite-derived cloud liquid water path, J. Appl. Meteor. Climatol., 48, 8, 1575-1590, doi:10.1175/2009JAMC2112.1.

Greuell, W., E. van Meijgaard, J. F. Meirink and N. Clerbaux, 2011: Evaluation of model predicted top-of-atmosphere radiation and cloud parameters over Africa with observations from GERB and SEVIRI, J. Climate, 24, 15, 4015-4036.

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., 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., H. M. Deneke, and A. J. Feijt, 2008a: Validation of cloud liquid water path retrievals from SEVIRI using one year of CloudNET observations , J. Appl. Meteor. Climatol., 47,1, 206 – 222.

Roebeling, R. A., S. Placidi, D. P. Donovan, H. W. J. Russchenberg, and A.J. Feijt, 2008b: Validation of liquid cloud property retrievals from SEVIRI using ground-based observations, Geophys. Res. Lett., 35, doi:10.1029/2007GL032115.

Roebeling, R. A., and E. van Meijgaard, 2009a: Evaluation of the diurnal cycle of model predicted cloud amount and liquid water path with observations from MSG-SEVIRI, J. Climate, 22, 1749-1766, DOI: 10.1175/2008JCLI2391.1

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

Roebeling, R.A., E.L.A. Wolters, J.F. Meirink en H. Leijnse, 2012: Triple Collocation of Summer Precipitation Retrievals from SEVIRI over Europe with Gridded Rain Gauge and Weather Radar Data J. Hydrometeor., 13, 1552-1566, doi:10.1175/JHM-D-11-089.1.

Schutgens, N. A. J. and R. A. Roebeling, 2009: Validating the validation: the influence of liquid water distribution in clouds on the intercomparison of satellite and surface observations, J. Atmos. Ocean. Tech. 26, 8, 1457-1474, doi:10.1175/2009JTECHA1226.1.

Teuling, A.J., C.M. Taylor, J.F. Meirink, et al., 2017: Observational evidence for cloud cover enhancement over western European forests, Nature Communications, 8, 14065, doi:10.1038/ncomms14065.

Wolters, E. L. A., R. A. Roebeling and A. J. Feijt, 2008: Evaluation of cloud phase retrieval methods for SEVIRI onboard METEOSAT-8 using ground-based lidar and cloud radar data, J. Appl. Meteor. and Clim, 47, 6, 1723-1738, doi:10.1175/2007JAMC1591.1.

Wolters, E. L. A., H. M. Deneke, B. J. J. M. van den Hurk, J. F. Meirink en R. A. Roebeling, 2010: Broken and inhomogeneous cloud impact on satellite cloud-phase retrieval J. Geophys. Res., 115, doi:10.1029/2009JD012205.

Wolters, E. L. A., B. J. J. M. van den Hurk, and R. A. Roebeling, 2011: Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH, Hydrol. Earth Syst. Sci., 15, 437-451, doi:10.5194/hess-15-437-2011.

Data Quality

Lineage

Source information

Level 1.5 data (spectral radiances) of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board Meteosat Second Generation (MSG).


Source citation

The European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT).


Processing steps

Processing description

The MSG-CPP algorithm consists of three steps. The first step is to separate cloud free from cloud contaminated and cloud filled pixels, which is done with a modified version of the cloud detection algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS) (J. Riédi, University of Lille). In the second step the primary cloud properties (Cloud Top Temperature, Cloud Phase, Cloud Optical Thickness and Cloud Particle size) are retrieved in an iterative manner by simultaneously comparing satellite observed reflectances at visible (0.6 um) and near-infrared (1.6 um) wavelengths to Look Up Tables (LUTs) of simulated reflectances of water and ice clouds for given optical thicknesses, particle sizes and surface albedos. These LUTs have been generated with the Doubling Adding KNMI (DAK) radiative transfer model (Stammes, 2001). The third step is to calculate the secondary cloud properties (Cloud Water Path, Cloud Droplet Number Concentration, Cloud Geometrical Thickness, Surface Solar Irradiance, Precipitation occurrence and intensity) for which the retrieval largely relies on the primary cloud properties. The retrievals are limited to satellite and solar viewing zenith angles smaller than 78°.

Algorithms used

Cloud Physical Properties (CPP) algorithm (C code) and the Doubling Adding KNMI (DAK) radiative transfer model (Fortran code).

Ancillary data

Surface albedo climatology from MODIS, water vapor climatology from ERA-INTERIM, and reflectance Look Up Tables from DAK.

Processing date

1 January 2012.

Data validation

Validation studies have been performed and published both in the peer-reviewed literature (see references above) and in CM-SAF validation reports. The below table gives an indication of the typical accuracy of the various products. It should be noted that the accuracy further depends on the scene type (retrieval errors are larger for inhomogeneous/broken cloudy scenes than for overcast conditions), surface type (e.g., retrievals error are large over bright, snow/ice-covered surfaces), and the viewing and illumination geometry (in general retrieval errors increase with viewing and solar zenith angle),


Table 1: List of MSG Cloud Physical Products and their reported validation accuracies.


ID Product Unit Accuracy
CLDMASK Cloud Fraction [-] 0.1
CPH Cloud Thermodynamic Phase [-] 0.1
COT Cloud Optical Thickness [-] 15%
REFF Particle size [m] -
CTT Cloud Top Temperature [K] 5 K
CTH Cloud Top Height [m] -
DCLD* Geometrical Depth [m] 250 m
DnDv* Droplet Number Concentration [m-3] -
CWP** Condensed Water Path [kg m-2] . 15 x 10-3 kg m-2
SDS Surface Downwelling Solar rad. [W m-2] 8 W m-2
SDS_CS Clear-Sky Surface Downwelling Solar rad. [W m-2] 7 W m-2
SDS_DIFF Surface Downwelling Solar Diffuse rad [W m-2] 7 W m-2
SDS_DIFF_CS . Clear-Sky Surface Downwelling Solar Diffuse rad . [W m-2] 11 W m-2
PRECIP Rain Rate [m s-1] 2.8 x 10-7 m s-1 (= 1 mm hr-1)

*): These products are only retrieved for liquid water clouds

**): Note, this is a combined product. The CWP for liquid water clouds represents the Liquid Water Path, CWP for ice clouds represents the Ice Water Path.

Spatial Data Organization

Indirect Spatial Reference

Map covers the MSG full disk, including Europe and Africa. See Figure 1 for an example.


Direct Spatial Reference Method

Raster.


Point and vector object information

Not applicable


Raster object information

Dataset title: cldmask (Cloud Mask Flag)

This variable was calculated from SEVIRI reflectances in the 0.6, 0.8, and 1.6 um channels and the radiances in the 3.8, 8.7, 10.8 and 12 um channels. The retrieval method is explained in more detail in Roebeling and van Meijgaard (2009a).


Dataset title: CPH (Cloud thermodynamic PHase )

This variable is retrieved with the CPP algorithm from the visible (0.6 um) and near-infrared (1.6 um) reflectances, complemented with brightness temperatures observed at 10.8 um. The retrieval method is explained in more detail in Wolters et al. (2008). Validation results are presented in Wolters et al. (2008); and Wolters et al. (2010a).


Dataset title: COT (Cloud Optical Depth)

This variable is retrieved with the CPP algorithm from the visible (0.6 um) and near-infrared (1.6 um) reflectances, complemented with brightness temperatures observed at 10.8 um. The retrieval method is explained in more detail in Roebeling et al. (2006),


Dataset title: Reff (Particle effective radius)

This variable is retrieved with the CPP algorithm from the visible (0.6 um) and near-infrared (1.6 um) reflectances, complemented with brightness temperatures observed at 10.8 um. The retrieval method is explained in more detail in Roebeling et al. (2006).


Dataset title: CTT (Cloud Top Temperature)

This variable was calculated from CPP Cloud Optical Depth (COT) and the brightness temperatures observed at 10.8 um. The retrieval method is explained in more detail in Feijt et al. (1999).


Dataset title: CCH (Cloud Column Height)

This variable is determined from the difference between the warmest Cloud Top Temperature (CTT_max) over an area of 100x100 SEVIRI pixels, which is assumed to represent a thin water cloud at an height of 1000 m, and the CTT of the observed pixel (CTT_act). The CCH is then calculated assuming that the vertical decrease in temperature obeys a wet adiabatic lapse rate of 6.5 K km-1.


Dataset title: DCLD (Water Cloud Geometrical Thickness)

This variable is calculated with a quasi-adiabatic cloud model (Roebeling et al., 2008b) that uses information on droplet effective radius and cloud optical thickness as input. The calculations are restricted to single layer water clouds (stratocumulus) that obey the assumption of an (quasi)-adiabatic profile.


Dataset title: DnDv (Cloud Droplet Number Concentration)

This variable is calculated with a quasi-adiabatic cloud model (Roebeling et al., 2008b) that uses information on droplet effective radius and cloud optical thickness as input. The calculations are restricted to single layer water clouds (stratocumulus) that obey the assumption of an (quasi)-adiabatic profile.


Dataset title: CWP (Condensed Water Path)

This variable was calculated from the CPP Cloud Optical Depth (COT) and cloud particle effective radius (REFF). The retrieval method is explained in more detail in Roebeling et al. (2006). Validation results are presented in Roebeling et al. (2008a); Greuell and Roebeling (2008); and Roebeling and van Meijgaard (2009a).


Dataset title: SDS (Surface Downwelling Solar radiation)

This variable was calculated from the CPP Cloud Optical Thickness (COT). The retrieval method and validation results are explained in more detail in Deneke et al. (2008); and Deneke et al. (2009).


Dataset title: PRECIP (Precipitation)

This variable is calculated from the CPP Cloud Water Path (CWP), Cloud Phase (CPH), cloud particle effective radius (Reff), and cloud column height. The retrieval method is explained in more detail in Roebeling and Holleman (2009). Validation results are presented in Holleman (2009); and Wolters et al. (2010b).

Spatial Reference

Coordinate System

Geographic coordinate units

km


Map projection

satellite view projection


Datum

WGS84


EPSG Code

Not applicable


PROJ4 parameters

+proj=geos +lon_0=0.000000 +lat_0=0 +h=35807.414063 +a=6378.169000

+b=6356.583984


Product Description Reference Information

Product Description Date

04-01-2011.


Product Description Review Date

14-11-2011.


Product Description Contact

Dr. Jan Fokke Meirink (meirink_at_knmi.nl)

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