This data set is only available for a restricted user group, please contact us if you want to access these data.
RESTRICTED only accessable in CEN/MPI net or via CliSAP login What does that mean?
CLARA-A2 stands for "CM SAF cLouds, Albedo RAdiation data record, AVHRR-based, Edition 2" and comprises a suite of radiation and cloud parameters derived within the EUMETSAT Climate Monitoring Satellite Application Facility (CM-SAF) from Advanced Very High Resolution Radiometer (AVHRR) data.
Here we offer (for internal use only) and briefly describe the cloud products. Note that we do not offer the Joint Cloud Property Histograms. Products are also available for surface albedo and radiation.
Cloud detection is carried out with the AVHRR-PPS package (see Dybbroe et al., 2005 under references of the CLARA-A2 albedo product), and is further described in the respective ATBD for cloud fraction. Cloud top parameters (pressure, altitude, temperature) are derived using two separate algorithms applied to opaque and fractional and or semi-transparent clouds, where modeled and measured radiances are matched with the aid of ERA-Interim profiles of meteorological data; see also the respective ATBD for cloud top parameters. Cloud top thermodynamic phase retrieval is based on a series of spectral tests applied to infrared brightness temperatures following Pavolonis and Heidinger (2004 and Pavolonis et al. (2005) (see references). By iteratively matching satellite-observed reflectances to look-up-tables of reflectances simulated with a RTM cloud optical thickness, effective cloud particle radius and cloud phase can be determined (e.g. Nakajima and King, 1992; Roebeling et al., 2006, see references). From these quantities the cloud liquid water path (CLWP) can be derived.The retrieval for ice water clouds is similar - except that different look-up-tables are used (see Hess et al., 1998 in the references). For more information we refer to the specific ATBD for cloud physical parameters.
Details about the processing steps in general can be found in the respectiveCM-SAF ATBD as well as in the papers listed in the references.
We offer edition 2 of the CLARA data set, i.e. CLARA-A2, for internal use only.
This data set was included in the ICDC data base: May 31, 2018
|Total cloud fraction (all day)||%|
|Standard deviation of total cloud fraction (all day)||%|
|Total daytime cloud fraction||%|
|Total nighttime cloud fraction||%|
|Cloud fraction (low-level clouds)||%|
|Cloud fraction (middle-level clouds)||%|
|Cloud fraction (high-level clouds)||%|
|Total day count||--|
|Count of daily means used for daytime cloud fraction||--|
|Count of cloud free classified nighttime observations||--|
|Cloud top temperature||K|
|Standard deviation of cloud top temperature||K|
|Cloud top altitude relative to surface altitude (GTOPO30)||m|
|Standard deviation of cloud top height||m|
|Cloud top pressure||hPa|
|Standard deviation of cloud top pressure||hPa|
|Count of daily means used for cloud top parameters||--|
|Fraction of liquid water clouds (all day)||%|
|Fraction of liquid water clouds standard deviation (all day)||%|
|Daytime fraction of liquid water clouds||%|
|Nighttime fraction of liquid water clouds||%|
|Count of daily means used for fraction of liquid water clouds (all day)||--|
|Count of daily means used for daylight fraction of liquid water cloud||--|
|Mean solar zenith angle (SZA)||degree|
|SZA standard deviation||degree|
|Cloud liquid water path (CLWP)||kg /m²|
|CLWP standard deviation||kg/m²|
|Mean CLWP retrieval error||kg/m²|
|Standard deviation of CLWP retrieval error||kg/m²|
|Water cloud mean optical thickness (tau)||--|
|Standard deviation of tau||--|
|Mean tau retrieval error||--|
|Standard deviation of tau retrieval error||--|
|Mean cloud droplet effective radius (Reff)||m|
|Reff standard deviation||m|
|Mean Reff retrieval error||m|
|Standard deviation of Reff retrieval error||m|
|Count of daily means available to compute CLWP||--|
|Mean solar zenith angle (SZA)||degree|
|SZA standard deviation||degree|
|Cloud ice water path (CIWP)||kg /m²|
|CIWP standard deviation||kg/m²|
|Mean CIWP retrieval error||kg/m²|
|Standard deviation of CIWP retrieval error||kg/m²|
|Ice cloud mean optical thickness (tau_ice)||--|
|Standard deviation of tau_ice||--|
|Mean tau_ice retrieval error||--|
|Standard deviation of tau_ice retrieval error||--|
|Mean ice particle effective radius (Reff_ice)||m|
|Reff_ice standard deviation||m|
|Mean Reff_ice retrieval error||m|
|Standard deviation of Reff_ice retrieval error||m|
|Count of daily means available to compute CIWP||--|
Period and temporal resolution:
- 1982-01 to 2015-12
- Monthly (daily data on request)
Coverage and spatial resolution:
- Spatial resolution: 0.25 degrees x 0.25 degrees, equally spaced lat/lon grid (Climate Modeling Grid, CMG)
- Geographic longitude:-179.875°E to 179.875°E
- Geographic latitude: -89.875°N to 89.875°N
- Dimension: 1440 columns x 720 rows
- Altitude: following terrain
Some of the offered products come with a retrieval uncertainty estimate, some others don't; all are provided, however, at least with a standard deviation of the mean which can serve an estimate of the uncertainty. The best sources to learn about the uncertainties and results of the validation are the CM-SAF Product User Guide (e.g. Table 6.1) and the CM-SAF Validation Report as well as the references.
In the following we briefly summarize a few of the limitations:
- For cloud fraction(s): Limitations arise for optically thin clouds (optical thickness < 0.15), and ice clouds over cold ground surfaces --> clouds may be missed; considerable underestimation arise during polar winter and under twilight conditions; false cloud may occur in areas of challenging surface emissivities, e.g. night-time desert.
- For cloud top parameters: In case of strong temperature inversions cloud top estimates become unreliable, this applies in general but in particular to polar regions and also to boundary layer clouds where cloud tops might be biased high by up to 1000m; problematic are optically very thin and/or multi-layered clouds as well as clouds being interpreted as opaque but are in fact diffuse and/or multi-layered in which case an under-estimation of the cloud top by up to 2000 m is possible
- For cloud physical properties like cloud phase, liquid and ice water path as well as optical thickness and effective particle radius difficulties arise in areas of high surface reflectances (ice & snow) and for highly 3-dimensional and/or broken cloud covers; the ice crystal shape used might not well represent the actual ice crystal shape distribution and hence effective ice particle size and cloud ice water paths might be biased considerably more often than their liquid counterparts; the smaller the actual penetration depth into the clouds is the more the retrieved parameters represent the near-cloud top properties rather than the mid- or low-level ones; the sensitivity to solar zenith angle (SZA) is quite high so that it is recommended to not use such data for SZA > 65°.
ICDC / CEN / University of Hamburg
email: stefan.kern (at) uni-hamburg.de
- Algorithm Theoretical Basis Document (ATBD)
- ATBD - Cloud Mask Product
- ATBD - Cloud Top Product
- ATBD - Cloud Physical Products AVHRR
- Product User Manual (PUM)
- Validation Report
- Karlsson, K.-G., et al., 2017, CLARA-A2: the second edition of the CM SAF cloud and radiation data record from 34 years of global AVHRR data, Atmospheric Chemistry and Physics, 17, 5809-5828, doi:10.5194/acp-17-5809-2017.
- Karlsson, K.-G., et al. 2015, Advancing the uncertainty characterisation of cloud masking in passive satellite imagery: Probabilistic formulations for NOAA AVHRR data, Remote Sens. Environ., 158, 126–139, doi:10.1016/j.rse.2014.10.028.
- Karlsson, K.-G., et al., 2013, CLARA-A1: a cloud, albedo, and radiation dataset from 28 yr of global AVHRR data, Atmospheric Chemistry and Physics, 13, 5351-5367, doi:10.5194/acp-13-5351-2013.
- Heidinger, A.K., et al., 2010, Deriving an inter-sensor consistent calibration for the AVHRR solar reflectance data record, International Journal of Remote Sensing, 31, 6493-6517.
- Roebeling, R. A., et al., 2006, Cloud property retrievals for climate monitoring: implications of differences between SEVIRI on METEOSAT-8 and AVHRR on NOAA-17, Journal of Geophysical Research-Atmospheres, 111, D20210, doi:10.1029/2005JD006990.
- Pavolonis, M. J., et al., 2005, Daytime global cloud typing from AVHRR and VIIRS: Algorithm description, validation and comparison, Journal of Applied Meteorology, 44, 804-826.
- Pavolonis, M. J., and A. K. Heidinger, 2004, Daytime cloud overlap detection from AVHRR and VIIRS, Journal of Applied Meteorology, 43, 762-778.
- Hess, M., et al., 1998, Optical properties of aerosol and clouds: The software package OPAC, Bulletin of the American Meteorological Society, 79(5), 831-844.
- Hess, M., et al., 1998, Scattering matrices of imperfect hexagonal crystals, Journal of Quantitative Spectroscopy and Radiative Transfer, 60(3), 301-308.
- Nakajima, T., and M. D. King, 1990, Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements, part 1: Theory, Journal of Atmospheric Sciences, 47, 1878-1893.
The citation for this data set is:
Karlsson, Karl-Göran; Anttila, Kati; Trentmann, Jörg; Stengel, Martin; Meirink, Jan Fokke; Devasthale, Abhay; Hanschmann, Timo; Kothe, Steffen; Jääskeläinen, Emmihenna; Sedlar, Joseph; Benas, Nikos; van Zadelhoff, Gerd-Jan; Schlundt, Cornelia; Stein, Diana; Finkensieper, Stephan; Håkansson, Nina; Hollmann, Rainer; Fuchs, Petra; Werscheck, Martin (2017): CLARA-A2: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 2, Satellite Application Facility on Climate Monitoring, DOI:10.5676/EUM_SAF_CM/CLARA_AVHRR/V002, https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V002.
All intellectual property rights of the CM SAF products belong to EUMETSAT. The use of these products is granted to every interested user, free of charge. If you wish to use these products, EUMETSAT copyright credit must be shown by displaying the words "copyright (year) EUMETSAT" on each of the products used.