Expected Outcome:

Project results are expected to contribute to two or more of the following expected outcomes:

• Optimal use of early observations. Evaluation and assessment of past observing methods and environmental factors, and on error analysis, quality control and bias adjustment of the in situ historical record;
• Better access and wider use of Copernicus relevant observations and auxiliary data collected during R&I projects not easily recoverable and reusable for validation purposes in an operational context;
• Enhanced availability and quality of in situ and ground-based data critical for the production, calibration and validation of Copernicus products and data services;
• Appropriate consideration of Copernicus Services’ cross-cutting challenges and R&I priorities.

Scope:

The areas of R&I to be explored to help addressed the above expected outcomes include:

1. Facilitation and demonstration of efficient, methodologically sound and sustainable reuse of in situ data collected during field campaigns and experiments for validation of Copernicus data and information services.
2. Development of innovative observation strategies and concepts to improve the observational capacity in selected data sparse areas. In the marine context, the gathering and qualification of acoustic observations to characterize marine ecosystems (e.g., micronekton) is an identified priority;
3. Synergistic use of complementary types of surface observations, such as pCO2 and pH observations from research vessels, ships of opportunities and Argo to improve the estimation of air/sea fluxes of CO₂;
4. Application of machine learning technologies for the quality control of historic and real-time meteorological and hydrological in-situ observations;

This approach should foster the exploitation of exiting in situ data capacities to close observation gaps in combination with new observing infrastructure and innovative processing/modelling techniques. The proposed developments should be interoperable, modular and scalable, should support the automatization of different processes orchestration, and proposals should provide a proof-of-concept or a prototype that can be easily adapted by at least one of the Copernicus Services and / or an observing network or similar delivering critical in situ data to Copernicus. The project shall demonstrate the applicability of the outcome for at least one of the Copernicus Services.

Depending on the selected area(s), relevant data providers, observing network operators, research infrastructures, and sensor manufactures should be involved in the proposal to the extent possible.

New in situ observation techniques and sensors should be considered and innovative solutions should be proposed for data collection, processing, quality control, and automation including the use of Artificial Intelligence and machine learning to ensure robust and non-biased high added value datasets.

In the selection of the topical scope of new-approaches to in-situ data and ground-based data collection, policy data needs should be considered, e.g. data needed for Green Deal monitoring, such as landscape features or parameters providing information on carbon farming.

Additionally, the transfer from research to operations should receive full attention during the course of the project to strengthen the readiness for an operational deployment in the future. This includes also cost estimates ensuring sustained operations. Appropriate interaction with the relevant Entrusted Entity of the Copernicus services, the conditions for making available, for re-using and exploiting the results (including IPR) by the said entities must be addressed during the project implementation. Developed software and collected observations should be open licensed.

Applicants are advised to consult information on the Copernicus programme in general at https://www.copernicus.eu/en and further details on the topic in the Guidance document.

In this topic the integration of the gender dimension (sex and gender analysis) in research and innovation content is not a mandatory requirement.

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