This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
G2.17
G2.17 Lack of a common effort in homogenization of MWR retrieval methods
Gap detailed description
Different retrieval methods are applied by different MWR manufacturers, operators, and users. Common retrieval methods include, but are not limited to, multivariate regression, neural networks and optimal estimation. This situation holds true for heterogeneous networks, such as those currently establishing in Europe. The uncertainty of MWR retrievals depends partially on the retrieval methods used, and the documentation and versioning of different methods are not usually easily accessible. Information on retrieval uncertainty is often completely missing. The traceability of software documentation and versioning is also not guaranteed. This impacts negatively on the harmonization of products provided by an heterogeneous MWR network. This gap shall be addressed at the network level. An attempt is currently being carried out within the EU COST action TOPROF. Progress will be reported on within GAIA-CLIM.
Activities within GAIA-CLIM related to this gap
The activities within GAIA-CLIM are to follow the developments at TOPROF and report to GAIA-CLIM as well as MWR users/manufacturers. Network-suitable retrieval methods are currently under development within TOPROF WG3. These activities will be followed and reported within the GAIA-CLIM project.
Gap remedy(s)
Remedy #1
Specific remedy proposed
The different types and flavors of the retrieval methods currently exploited shall be reviewed and reported. A common retrieval method is recommended for MWR belonging to a network. This task is currently tackled within the TOPROF WG3. A software package for a common retrieval method is expected within the next 2 years. The results of these activities will be followed and reported within the GAIA-CLIM project as recommendations for MWR network management.
Measurable outcome of success
A successful outcome is the dissemination of the availability of a network-suitable retrieval method to MWR manufacturers and users. An additional measure of success is the effective usage of the proposed network-suitable retrieval method in a MWR network such as the one currently establishing in Europe.
Achievable outcomes
Technological / organizational viability: high. The development is ongoing. The transfer to MWR network management faces some significant organizational challenges.
Indicative cost estimate: medium (>1million).
Relevance
The remedy will foster the use of a common network-suitable retrieval method. This will harmonise the MWR network products. This also helps to address G2.16, as better product harmonization leads to more solid characterization of uncertainties.
Timebound
2 years.
Gap risks to non-resolution
Identified future risk / impact |
Probability of occurrence if gap not remedied |
Downstream impacts on ability to deliver high quality services to science / industry / society |
Quality of MWR products varying throughout a network |
High |
Lack of network-harmonised MWR products |