Cloud

Generating long-term and coherent cloud property datasets that exploit multiple Earth observation missions

About

The Cloud_cci project covers the cloud component in the European Space Agency’s (ESA) Climate Change Initiative (CCI) programme.

In Cloud_cci, long-term and coherent cloud property datasets have been generated for which the synergistic capabilities of different Earth observation missions (European and non-European) were exploited.

A centrepiece of this activity has been the development and application of two state-of-the-art physical retrieval systems that use the optimal estimation technique for a simultaneous, spectrally consistent retrieval of cloud properties including pixel-based uncertainty measures:

The Community Cloud retrieval for Climate (CC4CL) which has been applied to AVHRR and AVHRR-heritage channels of MODIS, (A)ATSR and SLSTR
The Freie Universität Berlin AATSR-MERIS Cloud retrieval (FAME-C) applied to synergistic MERIS and AATSR measurements on-board ENVISAT

After producing prototype datasets (version 1), a series of multi-annual, global datasets were generated using the passive imager satellite sensors listed above. These datasets (version 2 hereafter) were comprehensively evaluated and documented. Updates of a subset of these datasets (version 3) benefited from retrieval improvements, were characterised by extended temporal coverage and included consistently derived radiation properties in addition to the cloud properties.

In the new project phase (Cloud_cci+) the focus is on the European sensors SEVIRI and SLSTR that provide higher spatial and/or temporal resolution than the previously used sensors. Additionally, the full spectral information of these sensors for the retrieval of cloud and radiation properties is being exploited.

Data

Data from the Cloud_cci project

Available data

Daily composite data (Level-3U) - global 0.05° lat/lon grid
Monthly mean and histogram data (Level-3C) - global 0.5° lat/lon grid

(Please also check out the Level-3U image browser)

Fig. Overview of Cloud_cci version 3 datasets and the time periods they cover.

Browsing and downloading version 3 datasets via the FTP Server

Level-3U data: https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3U/
Level-3C data: https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3C/

Downloading version 3 datasets using WGET

example WGET command to download entire AVHRR-PMv3 L3C dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3C/AVHRR-PM/

example WGET command to download entire AVHRR-PMv3 L3U dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3U/AVHRR-PM/

example WGET command to download entire AVHRR-AMv3 L3C dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3C/AVHRR-AM/

example WGET command to download entire AVHRR-AMv3 L3U dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3U/AVHRR-AM/

example WGET command to download entire ATSR2-AATSRv3 L3C dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3C/ATSR2-AATSR/

example WGET command to download entire ATSR2-AATSRv3 L3U dataset:

wget -r -nH -e robots=off --cut-dirs=9 --no-parent --reject="index.html*" https://public.satproj.klima.dwd.de/data/ESA_Cloud_CCI/CLD_PRODUCTS/v3.0/L3U/ATSR2-AATSR/

Alternative data access point

Level 3C products of version 2 and version 3 datasets can also be accessed via the CCI Open Data Portal.

Key Documents

Document name	Version	Issue date
Product Validation and Intercomparisons Report (PVIR) SEVIRI SLSTR	3.0	Nov. 20, 2023
Algorithm Theoretical Baseline Document (ATBD)	9.0	Nov. 20, 2023
Algorithm Theoretical Baseline Document (ATBD CC4CL)	v9.0	March 3, 2023
Climate Reserach Data Package (CRDP) SEVIRI SLSTR	3.0	Nov. 20, 2023
User Case Study 2 - Designing a “Sunny Vacation Map”	1.0	March 17, 2021

Document name	Version	Issue date
Cloud Product User Guide (PUG)	5.1	Jan. 16, 2020
Product Validation and Intercomparison Report (PVIR)	6.0	Feb. 3, 2020
Algorithm Theoretical Baseline Document (ATBD)	6.2	Oct. 14, 2019
Algorithm Theoretical Baseline Document (ATBD CC4CL)	6.2	Oct. 18, 2019

Document name	Version	Issue date
Cloud Product User Guide (PUG)	4.0	March 6, 2018
Product Validation and Intercomparison Report (PVIR)	5.1	March 6, 2018
Algorithm Theoretical Baseline Document (ATBD)	5.1	Sept. 12, 2017
Algorithm Theoretical Baseline Document (ATBD FAME C)	5.0	Sept. 12, 2017
Algorithm Theoretical Baseline Document (ATBD CC4CL)	5.1	Aug. 28, 2017
Climate Assessment Report (CAR)	3.1	Sept. 18, 2017
Comprehensive Error Characterisation Report (CECR)	4.1	April 30, 2018
Algorithm Theoretical Baseline Document (ATBD CC4CL TOAFLUX)	1.1	Oct. 14, 2019
Algorithm Theoretical Baseline Document (ATBD CC4CL MLEV)	1.1	May 31, 2016
Validation Report (CC4CL TOAFLUX)	1.0	April 30, 2018
Validation Report (CC4CL MLEV)	1.1	April 30, 2018
Technical Report on AVHRR GAC FCDR generation	1.0	May 10, 2017
Report on orbital drift correction for AVHRR	1.1	Aug. 28, 2017

Document name	Version	Issue date	Download
Workshop Programme	final version	-
(For accessing the Workshop presentations please contact Cloud_cci)	information	-

Team

The following people are part of the Cloud CCI team.

Dr. Martin Stengel, DWD – Science Leader
Daniel Philipp, DWD – EO Expert
Dr. Rainer Hollmann, DWD – EO Expert
Dr. Elisa Carboni, RAL – EO Expert
Dr. Gareth Thomas, RAL – Leader of EO Algorithm Development team
Prof. Roy Grainger, University of Oxford – EO Expert
Dr. Adam Povey, University of Oxford –EO Expert
Dr. Simon Proud, University of Oxford – EO Expert
Prof. P. Stier, University of Oxford – Leader of the Climate Research Group
Dr. Matthew Christensen, University of Oxford – Climate Research
A. Mayer, DWD – Climate Research

Publications

Click on the links below for publications relating to the Cloud project.

Philipp, D., Stengel, M., and Ahrens, B.: Analyzing the Arctic Feedback Mechanism between Sea Ice and Low-Level Clouds Using 34 Years of Satellite Observations. Journal of Climate, 33(17), 7479-7501. https://doi.org/10.1175/JCLI-D-19-0895.1, 2020.

Stengel, M., Stapelberg, S., Sus, O., Finkensieper, S., Würzler, B., Philipp, D., Hollmann, R., Poulsen, C., Christensen, M., and McGarragh, G.: Cloud_CCI Advanced Very High Resolution Radiometer post meridiem (AVHRR-PM) data set version 3: 35-year climatology of global cloud and radiation properties, Earth Syst. Sci. Data, 12, 41–60, https://doi.org/10.5194/essd-12-41-2020, 2020.

Feofilov, A. G. and Stubenrauch, C. J.: Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders, Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2019-166, in review, 2019.

Eliasson, S., Karlsson, K. G., van Meijgaard, E., Meirink, J. F., Stengel, M., and Willén, U.: The Cloud_CCI simulator v1.0 for the Cloud_CCI climate data record and its application to a global and a regional climate model, Geosci. Model Dev., 12, 829-847, https://doi.org/10.5194/gmd-12-829-2019, 2019.

Stengel, M., Schlundt, C., Stapelberg, S., Sus, O., Eliasson, S., Willén, U., and Meirink, J. F.: Comparing ERA-Interim clouds with satellite observations using a simplified satellite simulator, Atmos. Chem. Phys., 18, 17601-17614, https://doi.org/10.5194/acp-18-17601-2018, 2018.

Baró, R., Jiménez-Guerrero, P., Stengel, M., Brunner, D., Curci, G., Forkel, R., Neal, L., Palacios-Peña, L., Savage, N., Schaap, M., Tuccella, P., Denier van der Gon, H., and Galmarini, S.: Evaluating cloud properties in an ensemble of regional online coupled models against satellite observations, Atmos. Chem. Phys., 18, 15183-15199, https://doi.org/10.5194/acp-18-15183-2018, 2018.

Stengel, M., Stapelberg, S., Sus, O., Schlundt, C., Poulsen, C., Thomas, G., Christensen, M., Carbajal Henken, C., Preusker, R., Fischer, J., Devasthale, A., Willén, U., Karlsson, K.-G., McGarragh, G. R., Proud, S., Povey, A. C., Grainger, R. G., Meirink, J. F., Feofilov, A., Bennartz, R., Bojanowski, J. S., and Hollmann, R.: Cloud property data sets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_CCI project, Earth Syst. Sci. Data, 9, 881-904, https://doi.org/10.5194/essd-9-881-2017, 2017.

Sus, O., Stengel, M., Stapelberg, S., McGarragh, G., Poulsen, C., Povey, A. C., Schlundt, C., Thomas, G., Christensen, M., Proud, S., Jerg, M., Grainger, R., and Hollmann, R.: The Community Cloud retrieval for CLimate (CC4CL) – Part 1: A framework applied to multiple satellite imaging sensors, Atmos. Meas. Tech., 11, 3373-3396, https://doi.org/10.5194/amt-11-3373-2018, 2018.

McGarragh, G. R., Poulsen, C. A., Thomas, G. E., Povey, A. C., Sus, O., Stapelberg, S., Schlundt, C., Proud, S., Christensen, M. W., Stengel, M., Hollmann, R., and Grainger, R. G.: The Community Cloud retrieval for CLimate (CC4CL) – Part 2: The optimal estimation approach, Atmos. Meas. Tech., 11, 3397-3431, https://doi.org/10.5194/amt-11-3397-2018, 2018.

Keller, M., Kröner, N., Fuhrer, O., Lüthi, D., Schmidli, J., Stengel, M., Stöckli, R., and Schär, C.: The sensitivity of Alpine summer convection to surrogate climate change: an intercomparison between convection-parameterizing and convection-resolving models, Atmos. Chem. Phys., 18, 5253-5264, https://doi.org/10.5194/acp-18-5253-2018, 2018.

Merchant, C. J., Paul, F., Popp, T., Ablain, M., Bontemps, S., Defourny, P., Hollmann, R., Lavergne, T., Laeng, A., de Leeuw, G., Mittaz, J., Poulsen, C., Povey, A. C., Reuter, M., Sathyendranath, S., Sandven, S., Sofieva, V. F., and Wagner, W.: Uncertainty information in climate data records from Earth observation, Earth Syst. Sci. Data, 9, 511-527, https://doi.org/10.5194/essd-9-511-2017, 2017.

Christensen, M. W., Neubauer, D., Poulsen, C. A., Thomas, G. E., McGarragh, G. R., Povey, A. C., Proud, S. R., and Grainger, R. G.: Unveiling aerosol–cloud interactions – Part 1: Cloud contamination in satellite products enhances the aerosol indirect forcing estimate, Atmos. Chem. Phys., 17, 13151-13164, https://doi.org/10.5194/acp-17-13151-2017, 2017.

Neubauer, D., Christensen, M. W., Poulsen, C. A., and Lohmann, U.: Unveiling aerosol–cloud interactions – Part 2: Minimising the effects of aerosol swelling and wet scavenging in ECHAM6-HAM2 for comparison to satellite data, Atmos. Chem. Phys., 17, 13165-13185, https://doi.org/10.5194/acp-17-13165-2017, 2017.

Stubenrauch, C. J., Feofilov, A. G., Protopapadaki, S. E., & Armante, R. (2017). Cloud climatologies from the infrared sounders AIRS and IASI: strengths and applications. Atmospheric Chemistry and Physics, 17(22), 13625.

Protopapadaki, S. E., Stubenrauch, C. J., and Feofilov, A. G.: Upper tropospheric cloud systems derived from IR sounders: properties of cirrus anvils in the tropics, Atmos. Chem. Phys., 17, 3845-3859, doi:10.5194/acp-17-3845-2017, 2017.

Lauer, A., Eyring, V., Righi, M., Buchwitz, M., Defourny, P., Evaldsson, M., Friedlingstein, P., de Jeu, R., de Leeuw, G., Loew, A., Merchant, C. J., Müller, B., Popp, T., Reuter, M., Sandven, S., Senftleben, D., Stengel, M., van Roozendael, M., Wenzel, S. and Willen, U. (2017) Benchmarking CMIP5 models with a subset of ESA CCI Phase 2 data using the ESMValTool. Remote Sensing of Environment, 203, pp.9-39.

Povey, A. C. and Grainger, R. G.: Known and unknown unknowns: uncertainty estimation in satellite remote sensing, Atmos. Meas. Tech., 8, 4699-4718, doi:10.5194/amt-8-4699-2015, 2015.

Bojanowski, J.S., Stöckli, R., Tetzlaff, A., Kunz, H., 2014. The Impact of Time Difference between Satellite Overpass and Ground Observation on Cloud Cover Performance Statistics. Remote Sensing 6(12), 12866-12884.

Carbajal Henken, C. K., Doppler, L., Lindstrot, R., Preusker, R., and Fischer, J.: Exploiting the sensitivity of two satellite cloud height retrievals to cloud vertical distribution, Atmos. Meas. Tech., 8, 3419-3431, doi:10.5194/amt-8-3419-2015, 2015.

Feofilov, A.G., C. J. Stubenrauch, and J. Delanoë, Ice water content vertical profiles of high-level clouds: classification and impact on radiative fluxes, Atmos. Chem. Phys., 15, 12327–12344, doi:10.5194/acp-15-12327-2015, 2015

Hollstein, A., Fischer, J., Carbajal Henken, C., and Preusker, R.: Bayesian cloud detection for MERIS, AATSR, and their combination, Atmos. Meas. Tech., 8, 1757-1771, doi:10.5194/amt-8-1757-2015, 2015

Keller, M., O. Fuhrer, J. Schmidli, M. Stengel, R. Stöckli, and C. Schär (2015): Evaluation of convection-resolving models using satellite data: The diurnal cycle of summer convection over the Alps. Meteorol. Z., doi:10.1127/metz/2015/0715

Stengel, M., S. Mieruch, M. Jerg, K.-G. Karlsson, R. Scheirer, B. Maddux, J.F. Meirink, C. Poulsen, R. Siddans, A. Walther, R. Hollmann., 2015: The Clouds Climate Change Initiative: Assessment of state-of-the-art cloud property retrieval schemes applied to AVHRR heritage measurements. Remote Sensing of Environment, 162, 363-379.

Carbajal Henken, C. K., Lindstrot, R., Preusker, R., and Fischer, J.: FAME-C: cloud property retrieval using synergistic AATSR and MERIS observations, Atmos. Meas. Tech., 7, 3873-3890, doi:10.5194/amt-7-3873-2014, 2014

Karlsson, Karl-Göran, and Erik Johansson. 2014: "Multi-Sensor calibration studies of AVHRR-heritage channel radiances using the simultaneous nadir observation approach." Remote Sensing 6.3 (2014): 1845-1862.

Hollmann, R., Merchant, C., Saunders, R., Downy, C., Buchwitz, M., Cazenave, A., Chuvieco, E., Defourny, P., Leeuw, G. de, Forsberg, R., Holzer-Popp, T., Paul, F., Sandven, S., Sathyendranath, S., Roozendael, M. van, & Wagner W. (2013). The ESA Climate Change Initiative: satellite data records for essential climate variables, Bulletin of the American Meteorological Society, doi: 10.1175/BAMS-D-11-00254.1

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