Articles | Volume 9, issue 2
Earth Syst. Sci. Data, 9, 881–904, 2017
Earth Syst. Sci. Data, 9, 881–904, 2017

  23 Nov 2017

23 Nov 2017

Cloud property datasets retrieved from AVHRR, MODIS, AATSR and MERIS in the framework of the Cloud_cci project

Martin Stengel1, Stefan Stapelberg1, Oliver Sus1, Cornelia Schlundt1, Caroline Poulsen2, Gareth Thomas2, Matthew Christensen2, Cintia Carbajal Henken3, Rene Preusker3, Jürgen Fischer3, Abhay Devasthale4, Ulrika Willén4, Karl-Göran Karlsson4, Gregory R. McGarragh5, Simon Proud5, Adam C. Povey6, Roy G. Grainger6, Jan Fokke Meirink7, Artem Feofilov8, Ralf Bennartz9,10, Jedrzej S. Bojanowski11, and Rainer Hollmann1 Martin Stengel et al.
  • 1Deutscher Wetterdienst, Frankfurter Str. 135, 63067 Offenbach, Germany
  • 2Rutherford Appleton Laboratory, Didcot, Oxfordshire, UK
  • 3Institute for Space Sciences, Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
  • 4Swedish Meteorological and Hydrological Institute (SMHI), Norrköping, Sweden
  • 5Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, Oxford OX1 3PU, UK
  • 6National Centre for Earth Observation, University of Oxford, Oxford, OX1 3PU, UK
  • 7Royal Netherlands Meteorological Institute (KNMI), De Bilt, the Netherlands
  • 8Laboratoire de météorologie dynamique (LMD), Paris, France
  • 9University of Wisconsin, Madison, Wisconsin, USA
  • 10Vanderbilt University, Nashville, Tennessee, USA
  • 11MeteoSwiss, Zurich, Switzerland

Abstract. New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.

For each dataset a digital object identifier has been issued:

Cloud_cci AVHRR-AM:

Cloud_cci AVHRR-PM:

Cloud_cci MODIS-Terra:

Cloud_cci MODIS-Aqua:

Cloud_cci ATSR2-AATSR:

Cloud_cci MERIS+AATSR:

Short summary
We present new cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS. Retrieval systems were developed that include cloud detection and cloud typing followed by optimal estimation retrievals of cloud properties (e.g. cloud-top pressure, effective radius, optical thickness, water path). Special features of all datasets are spectral consistency and rigorous uncertainty propagation from pixel-level data to monthly properties.