Articles | Volume 6, issue 2
Geosci. Instrum. Method. Data Syst., 6, 453–472, 2017
Geosci. Instrum. Method. Data Syst., 6, 453–472, 2017

Research article 06 Nov 2017

Research article | 06 Nov 2017

Making better sense of the mosaic of environmental measurement networks: a system-of-systems approach and quantitative assessment

Peter W. Thorne1, Fabio Madonna2, Joerg Schulz3, Tim Oakley4,5, Bruce Ingleby6, Marco Rosoldi2, Emanuele Tramutola2, Antti Arola7, Matthias Buschmann8, Anna C. Mikalsen9, Richard Davy9, Corinne Voces1, Karin Kreher10, Martine De Maziere11, and Gelsomina Pappalardo2 Peter W. Thorne et al.
  • 1Irish Climate Analysis Research UnitS, Department of Geography, Maynooth University, Maynooth, Ireland
  • 2Consiglio Nazionale delle Ricerche, Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA), Potenza, Italy
  • 3EUMETSAT, Darmstadt, Germany
  • 4Global Climate Observing System Secretariat, Geneva, Switzerland
  • 5Met Office, Fitzroy Road, Exeter, EX1 3PB, UK
  • 6European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 7Finnish Meteorological Institute, Helsinki, Finland
  • 8Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 9Nansen Environmental and Remote Sensing Center, Bergen, Norway
  • 10BK Scientific, Mainz, Germany
  • 11Royal Belgian Institute for Space Aeronomy (BIRA-IASB), Brussels, Belgium

Abstract. There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA–CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is correct. Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA–CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA–CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.

Short summary
The term system-of-systems with respect to observational capabilities is frequently used, but what does it mean and how can it be assessed? Here, we define one possible interpretation of a system-of-systems architecture that is based upon demonstrable aspects of observing capabilities. We develop a set of assessment strands and then apply these to a set of atmospheric observational networks to decide which observations may be suitable for characterising satellite platforms in future work.