G3.05 Representativeness uncertainty assessment missing for higher-level data based on averaging of individual measurements

Gap abstract: 

Level-3 data are, by definition, constructed by averaging asynoptic level-2 data over certain space-time intervals, so as to arrive at a (regularly) gridded data product. However, the (global) sampling pattern of the sounder(s) that produced the original level-2 data is never perfectly uniform, nor are revisit times short enough to guarantee dense and homogeneous temporal sampling of e.g. a monthly mean at high horizontal resolution. Consequently, the averages may deviate substantially from the true average field that would be obtained if complete spatiotemporal coverage were possible. These so-called representativeness errors are only rarely investigated, and almost never provided with a product, in spite of their importance in interpreting the data.

Part I Gap description

Primary gap type: 
  • Knowledge of uncertainty budget and calibration
Secondary gap type: 
  • Uncertainty in relation to comparator measures
  • Governance (missing documentation, cooperation etc.)
ECVs impacted: 
  • Temperature,Water vapour, Ozone, Aerosols, Carbon Dioxide, Methane
User category/Application area impacted: 
  • Operational services and service development (meteorological services, environmental services, Copernicus Climate Change Service (C3S) and Atmospheric Monitoring Service (CAMS), operational data assimilation development, etc.)
  • International (collaborative) frameworks and bodies (space agencies, EU institutions, WMO programmes/frameworks etc.)
  • Climate research (research groups working on development, validation and improvement of ECV Climate Data Records)
Non-satellite instrument techniques involved: 
  • Radiosonde
  • Ozonesonde
  • Lidar
  • Microwave Radiometer
  • FTIR
  • Brewer/Dobson
  • UV/VIS zenith DOAS
  • Pandora
  • G 3.01. To be addressed before G3.05

    Argument: A quantification of representativeness uncertainties requires an adequate representation of the atmosphere at the scale of the measurements

Detailed description: 

The creation of level-3 data by averaging non-uniformly distributed level-2 measurements inevitably leads to representativeness errors, see e.g. Coldewey-Egbers et al., (2015) for the case of a level-3 (gridded monthly means) total ozone data set. The resulting representativeness uncertainty can be larger than the formal uncertainty on the mean. In the best case this would represent an additional random uncertainty term. If the sampling pattern of the sounder changes in time, this may give rise to systematic, time-dependent representativeness errors that affect for example trend analyses for climate research (see e.g. Damadeo et al., 2014).  However, estimates of these representativeness uncertainties are rarely included with the data product. Also, the representativeness of the ground-based network should be taken into account when validating such data sets, i.e. the sparse spatial and temporal sampling of the ground network leads to significant representativeness uncertainties in for instance derived monthly (zonal) means.

Note that also in the context of validation of level-2 data, measurements are sometimes averaged after co-location (e.g. Valks et al., 2011; Schneising et al.,2012) without explicit calculation of the representativeness errors and resulting uncertainty. 


Operational space missions or space instruments impacted: 
  • Independent of specific space mission or space instruments
Validation aspects addressed: 
  • Gridded product (Level 3)
  • Time series and trends
  • Representativity (spatial, temporal)
Gap status after GAIA-CLIM: 
  • After GAIA-CLIM this gap remains unaddressed

This gap will remain as it was not addressed within the project (level-3 and level-4 data were in general not addressed within the project).

Part II Benefits to resolution and risks to non-resolution

Identified benefitUser category/Application area benefittedProbability of benefit being realisedImpacts
More complete uncertainty quantification on the reported data.
  • All users and application areas will benefit from it
  • High
Better uncertainty characterization. This in turn increases confidence in the data for the end user and allows more meaningful use in a variety of applications.
Identified riskUser category/Application area at riskProbability of risk being realisedImpacts
Underestimated uncertainty on the reported data (averages)
  • All users and application areas will suffer from it.
  • High
Incomplete data characterization and potentially limited or flawed interpretation, whatever the use type.

Part III Gap remedies

Gap remedies: 

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
  • Coldewey-Egbers et al., The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative, AMT v8, 2015

  • Damadeo et al.,: Reevaluation of stratospheric ozone trends from SAGE II data using a simultaneous temporal and spatial analysis,  Atmos. Chem. Phys., 14, 2014

  • Lambert, J.-C., et al., Comparing and merging water vapour observations: A multi-dimensional perspective on smoothing and sampling issues, in Monitoring Atmospheric Water Vapour: Ground-Based Remote Sensing and In-situ Methods, N. Kämpfer (Ed.), ISSI Scientific Report Series, Vol. 10, Edition 1, 326 pp., ISBN: 978-1-4614-3908-0, DOI 10.1007/978-1-4614-3909-7_2, © Springer New York 2012

  • Schneising et al., Atmospheric greenhouse gases retrieved from SCIAMACHY: comparison to ground-based FTS measurements and model results, ACP v12, 2012

  • Valks et al., Operational total and tropospheric NO2 column retrieval for GOME-2, AMT v4, 2011