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Traceability model diagrams

The instrument model diagram consists in a visual sketch of the processing steps leading to a product and its traceability chain. Following the GAIA-CLIM Guidance note 'Guide to Uncertainty in Measurement & its Nomenclature' (final Version 4), the way to metrological robust uncertainty analysis and traceability chain starts from clearly displaying the whole process (main chain) in terms of three models: the physical, processing, and metrological (traceability) models. The physical model describes the physics behind each stage of the process that contributes to the measurements taken, including all of the physical processes associated with the measurand detection. The processing model considers how the raw data collected is processed to provide the end product, through calibration to final geophysical parameter. The metrological model describes the set of linkages of a measurement to a reference standard. The aim is to determine what the fundamental reference for the measurement is. The components used in the traceability chains (shown in the Figure) have been determined through consultation with the QA4ECV project.  The set used have been expanded from those used in QA4ECV to accommodate the differing needs of the GAIA-CLIM project.


Figure: The components used in the traceability chains produced under the GAIA-CLIM project.

 Model diagrams for the following measurement techniques have been produced by WP2:


MWR instrument model diagram and associated uncertainty

The main chain displays the process of producing a geophysical product from the instrument measurements.

Figure 1: Main chain of the MWR instrument.


The physical model chain of the MWR measurement displays the physical processes associated with the measurand detection and calibration. For ground-based MWR, the primary measurand is the natural down-welling atmospheric radiance, collected by the antenna and transformed into voltages by the detector, and finally calibrated into brightness temperature (Tb).

Figure 2: Physical model chain of the MWR measurement.


The processing model chain displays the calculation steps that the data undergo to obtain the final measurand of interest, e.g. temperature and humidity profile retrievals. The input is derived from the previous step (calibration) and each output leads to the next step in the processing chain.

Figure 3: Processing model chain of the MWR instrument.

The metrological model chain describes the flow diagram of the measurement, including references to calibration, uncertainty sources (both from random and systematic effects), and linkages to reference standards. The aim of the metrological chain is to demonstrate that linkage to a reference standard is achieved. In some cases, it will be possible to obtain full metrological traceability - that is, an unbroken chain of calibrations back to the International System of Units (SI). In other cases, however, such a complete chain may not be possible. It is important, however, to show what references do exist.

Figure 4: Metrological model chain of the MWR measurement.

GAIA-CLIM aims to provide reference grade products, providing metrological model chains encapsulated in a “how to measure” guide describing the individual products. The ultimate goal is to produce metrologically-rigorous traceable measurements for the target measurement systems, providing practical coverage factors. For those techniques with sufficient maturity, an open-literature paper describing the product, the uncertainty, and its traceability shall be published.


GAIA-CLIM home page

What is the problem/issue that was addressed?

The H2020 GAIA-CLIM project (2015 - 2018) aimed to establish sound methods for the characterisation of satellite-based Earth Observation (EO) data by surface-based and sub-orbital measurements (non-satellite measurements).

Why is it important for society?

The Copenicus program activities, if successful, shall enable a step-change in our ability to use and exploit environmental data to the benefit of society. A crucial component of the Copernicus framework is provision of high-quality observational datasets from satellites. These need to be calibrated and validated to standards that enable them to be used with confidence for applications. This requires ancillary datasets from in-situ and other sources of high-quality and sufficient quantity to robustly characterise sensor performance and radiative-transfer modelling. The challenges to rigorous satellite-data characterisation are formidable, because without traceability in the comparator measures, there is ambiguity in any comparison.

What were the overall objectives?

The objectives of GAIA-CLIM were to play a full role in supporting Copernicus. Firstly, by taking concrete steps to improve our capabilities to exploit current non-satellite observations to characterize satellite data. Secondly, by establishing prioritized needs for further observational capacity targeted at providing the required step-change in satellite calibration and validation capability.

The consortium brought together scientific, technical and leadership expertise in high-quality in-situ and sub-orbital observations, gap analyses, modelling, satellite operations and data assimilation, and in setting the priorities for the EO community.

Robust EO instrument characterisation is about significantly more than simply where and when a given set of EO and ground-based / sub-orbital measurements is taken. It requires, in addition, quantified uncertainty estimation for the reference measurements and an understanding of additional uncertainties that accrue and increase the apparent discrepancy between measured data sets through mismatches in spatio-temporal sampling. It is also about enabling like-for-like comparisons through data transformations such as simulation of Top-of-Atmosphere (TOA) radiances.

Finally, it needs user tools, which include statistical tools and the integrating capabilities afforded by data assimilation systems, to enable users to access and work with the data, including the uncertainty information. GAIA-CLIM has shown how this could be approached via its ‘Virtual Observatory’ demonstrator tool for a selection of well characterised ground-based / sub-orbital measurements and satellite co-locations.


Key activities under GAIA-CLIM have been to:

  • Define data quality by assessable attributes, such as traceablity, documentation, metadata retention, uncertainty quantification.
  •  Map observational capabilities.
  • Improve understanding and uncertainty quantification of selected in-situ, ground-based, and sub-orbital measurements.
  • Better quantify the impacts of inevatable measurement mismatches between satellite observations and the non-satellite measurements .
  • Use Data Assimilation to improve the usefulness of high-quality measurements in characterising satellite performance.
  • Provide useable and actionable both satellite and non-satellite data and tools to end users to improve their value for a broad range of applications.



The fifth and final version of the Gaps Assessment and Impacts Document (GAID) is now available!

The objective of this GAID was to identify and assess – through careful analysis against both existing and envisaged user requirements – yet unfulfilled user needs (‘gaps’) in the observation capability of Essential Climate Variables (ECVs) within the sphere of the GAIA-CLIM project.

The impact assessment focused on the availability of, and ability to utilize, truly reference quality traceable measurements in support of the long-term sustained space-borne monitoring of a set of ECVs. The GAIA-CLIM primary atmospheric ECVs specifically were temperature, water vapour (H2O), ozone (O3), carbon dioxide (CO2), methane (CH4), and aerosols. Because these ECVs are being monitored through the EUMETSAT operational satellite programme, the Copernicus Space Segment, and ESA research satellites, as well as by non-EU satellites, the relevance of the gaps and impact assessment is not limited to Europe. Nevertheless some focus in the project is placed on the European infrastructure.Figure: Assessment of gaps and impacts has been iterative and included external input.

The gaps impact assessment and discussion of potential remedies was organised per gap type in order to identify, e.g., similarity and/or complementarity between the listed gaps that originate from different work packages. The gap identification and assessment and subsequent impact discussions has been continued during the project. The GAID was therefore a living document and several versions of this document have been produced throughout the project. Both, the list of gaps and the impact assessment evolved. Gaps were regularly identified and updated from the project work packages. User needs were further obtained from the GAIA-CLIM user survey and user workshops, as well as through a travelling 'Roadshow' addressed at key stakeholders.

D6.2 - GAID v1


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