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GAIA-CLIM has been coordinated by the Nansen Environmental and Remote Sensing Center (NERSC) and the National University of Ireland Maynooth (NUIM). The project has been running from March 2015 to February 2018. This homepage will be available for another five years on best endeavour basis. In case of questions concerning GAIA-CLIM, please contact the scientific lead, Peter Thorne.

Scientific Lead

Peter Thorne
Maynooth University Department of Geography
Maynooth, Co. Kildare
Mobile: +353 87 612 2753
Email: peter.thorne"at"

Project Coordinator

Anna Christina Mikalsen
Nansen Environmental and Remote Sensing Center
Thormøhlens gate 47
N-5006 Bergen 
Go to Nansen Environmental and Remote Sensing Center's home page for details





The main results of the GAIA-CLIM project were:

  • The Maturity Matrix Assessment (MMA) developed within GAIA-CLIM to assess various quantifiable aspects of the maturity of a measurement system or network of measurement systems.
  • Product Traceability and Uncertainty (PTU) documents that represent a consistent metrological (measurement science) based approach to understanding measurement systems and their processing. Their key foundation is a traceability diagram that fully outlines the processing steps from SI units or internationally agreed standards to the final reported measurement.
  • Methods developed for - and applied to - the quantification of smoothing and sampling issues in a range of atmospheric ground-based measurement techniques, and to estimate the uncertainties that need to be taken into account when comparing non-perfectly co-located ground-based and satellite measurements with different spatio-temporal smoothing and sampling properties. Libraries (i.e. guiding material and data files) of such uncertainties for key ground-based measurement systems and for key comparisons have been made available.
  • The 'the ‘GRUAN Processor’ tool, which enables the comparison of co-located geophysical fields and simulated brightness temperatures between radiosondes and model fields.
  • The GAIA-CLIM Virtual Observatory, providing a unified platform for co-located observations, with statistics and uncertainty propagation information for both level-1 and level-2 products.
  • The gap assessment of unfulfilled user needs in observation capability of Essential Climate Variables (ECVs) within the sphere of the GAIA-CLIM project, which is sumarised in the Gaps Assessment and Impacts Document (GAID). The full details on all identified gaps are available in an online Catalogue of Gaps.
  • The Recommendations document that is a high-level accessible document, destilling the findings from the gap analysis performed within GAIA-CLIM into a set of 11 actionable priority recommended activities.



GRUAN Processor

The characterisation of uncertainties in NWP models is a major challenge that has been addressed as part of GAIA-CLIM WP4. In this regard, radiosonde observations from the GCOS Reference Upper-Air Network (GRUAN) have been used at the Met Office and ECMWF to assess uncertainties associated with model data.

A software tool has been developed based on a core radiative transfer modelling capability built around two existing open-source software packages (EUMETSAT’s NWP-SAF RTTOV fast radiative transfer model and Radiance Simulator). This software, referred to as the ‘GRUAN Processor’, enables the comparison of co-located geophysical fields and simulated brightness temperatures between radiosondes and model fields. A key innovation of the GRUAN Processor is the conversion of geophysical profiles into Top of the Atmosphere (TOA) radiance equivalents via RTTOV that enables comparisons in level-1 space that include an expression of the uncertainty in the reference in-situ profile data.

The GRUAN Processor has been developed as a stand-alone module, decoupled from the NWP systems at the Met Office and ECMWF, which will enable the capability to be developed and maintained on longer term as part of the core capability of the Virtual Observatory.

Post-processed outputs of the GRUAN Processor (monthly averaged profiles per GRUAN station) are publicly available on a demonstrator web-page.



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