GAIA-CLIM | Gap Analysis for Integrated Atmospheric ECV CLImate Monitoring

Summary
There is a recognised need for establishing sound methods for the characterisation of satellite-based Earth Observation (EO) data by surface-based and sub-orbital measurement platforms - spanning Atmosphere, Ocean and Land observations and the entire radiance spectrum. 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 through mismatches in sampling location and time and the distinct measurement footprints to enable a complete mapping of the reference measurements onto EO measurements. It also 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 in a ‘virtual observatory’ setting. It is only if robust uncertainty estimates are placed on the ground-based and sub-orbital data and used in the analysis that unambiguous interpretation of EO sensor performance can occur.
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More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/640276
Start date: 01-03-2015
End date: 28-02-2018
Total budget - Public funding: 5 999 726,25 Euro - 5 999 726,00 Euro
Cordis data

Original description

There is a recognised need for establishing sound methods for the characterisation of satellite-based Earth Observation (EO) data by surface-based and sub-orbital measurement platforms - spanning Atmosphere, Ocean and Land observations and the entire radiance spectrum. 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 through mismatches in sampling location and time and the distinct measurement footprints to enable a complete mapping of the reference measurements onto EO measurements. It also 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 in a ‘virtual observatory’ setting. It is only if robust uncertainty estimates are placed on the ground-based and sub-orbital data and used in the analysis that unambiguous interpretation of EO sensor performance can occur.

Status

CLOSED

Call topic

EO-3-2014

Update Date

27-10-2022
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Horizon 2020
H2020-EU.2. INDUSTRIAL LEADERSHIP
H2020-EU.2.1. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies
H2020-EU.2.1.6. INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies – Space
H2020-EU.2.1.6.0. Cross-cutting call topics
H2020-EO-2014
EO-3-2014 Observation capacity mapping in the context of Atmospheric and Climate change monitoring