Skip navigation.
Home

The transition from project phase 1 to 2 - From prototype data to long-term climate datasets

The ESA Cloud_cci project started in 2010 along with 12 other CCI projects covering atmospheric, oceanic and terrestrial ECV data products (Hollmann et al. 2013). The main goal is the generation of satellite-based climate data records (CDRs) that meet the challenging requirements of the Global Climate Observing System (GCOS 2011).

In phase 2 (2014 - 2016) Cloud_cci is aimed at the generation of 2 multi-decadal coherent global data sets (see Figure below) for GCOS cloud property ECVs including uncertainty estimates based on inter-calibrated radiances from:

  1. AVHRR/MODIS/(A)ATSR time series from 1982 to 2014 and
     
  2. MERIS/AATSR time series from 2002 to 2012, which will be extended by OLCI/SLSTR on-board Sentinel-3.

In phase 1 (2010 - 2013) prototype algorithms (CC4CL and FAME-C) were developed and 2007 - 2009 time series of both data sets ("demonstrator") were processed.
 


Figure 2 Overview of Cloud_cci datasets and the time periods they cover.

 

ESA Cloud_cci Phase2: reprocessing, validation and assessment of CDRs (1982 – 2014)

  • Further algorithm improvements (e.g., multi-layer clouds, cloud phase identification) in the framework of feedback loops
  • Development of methods for bridging interrupted time series, e.g. gap between ENIVSAT and SENTINEL-3
  • Development of a cloud-simulator package to strengthen the application of Cloud_cci products for global and regional climate model analysis
  • Comprehensive validation with well established satellite data records, surface observations and cloud climatologies (e.g., PATMOS-X, ISCCP, CLARA-A2, MODIS collection 6)

 

Benefits for user community – Added value of ESA Cloud_cci products

  • Spectral consistency of derived parameters, which is achieved by an optimal estimation (OE) approach based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared.
  • Uncertainty characterization, which will be inferred by the application of the OE approach as physically consistent single pixel uncertainty estimation and further propagated to the final product.
  • Increased temporal resolution by including multiple polar-orbiting satellite instruments, which also allows for mature cloud property histograms on 0.5° resolution due to high increased sampling rate.
     
  • Comprehensive assessment and documentation of the retrieval schemes and the derived cloud property datasets including the exploitation of applicability for evaluation of climate models and reanalyses..

 

Achievements in phase 1:

  • Open community algorithm CC4CL (AVHRR/MODIS/AATSR) and FAME-C (MERIS/AATSR), both based on OE
  • Publications on round robin, algorithms, inter-calibration, validation/evaluation
  • Prototype system and round robin algorithm evaluation framework