Remedy1: Quantification of representativeness of averages using modelling, statistical and sub-sampling techniques

Primary gap remedy type: 
Secondary gap remedy type: 
Proposed remedy description: 

Studies are required quantifying the representativeness of averages, e.g. by model-based simulations of averages based on either the limited real sampling or on an ideal, complete sampling. This approach was followed for instance by Coldewey-Egbers (2015) for a total ozone L3 product. More pragmatically, representativeness uncertainties can also be computed as a function of parametrized measurement inhomogeneity and climatological field variability (for instance Sofieva et al., 2014). Note that the demand for such studies is also a governance issue: service providers and overarching frameworks should insist that any L3 data set comes with such a quantification of representativeness uncertainties.

The effort required to address this gap depends on the particular product and on whether atmospheric variability is well understood for that ECV (c.f. gap G3.01). For most of the ECVs targeted by GAIA-CLIM, an estimate of the representativeness uncertainty should be achievable at a low cost.  The additional validation required to assess the quality of this representativeness uncertainty estimate may in absence of existing reference data sets at sufficiently high spatial and temporal sampling–  require a more significant investment, e.g. to conduct intensive field campaigns. 


This remedy directly addresses and fills the gap. 

Measurable outcome of success: 

Success is achieved when level-3 data sets include not only the formal uncertainty on the mean and the variance around that mean, but also an estimate of the representativeness uncertainty on that mean.  The reliability of this reported representativeness uncertainty must than also be validated or verified. 

Expected viability for the outcome of success: 
  • High
Scale of work: 
  • Single institution
  • Consortium
Time bound to remedy: 
  • Less than 3 years
Indicative cost estimate (investment): 
  • Low cost (< 1 million)
Indicative cost estimate (exploitation): 
  • No
Potential actors: 
  • EU H2020 funding
  • Copernicus funding
  • National funding agencies
  • National Meteorological Services
  • ESA, EUMETSAT or other space agency
  • Academia, individual research institutes