What is this issue, and what is its position in Horizon Europe?

It is a one-stage Research and Innovation Action in Cluster 5 (Climate, Energy and Mobility), Destination “Climate sciences and responses towards the transformation towards climate neutrality. The specific objective is to enhance climate and weather forecasting in Africa through improved observations, process knowledge, modeling, and African-European co-created climate science.

What are the milestones and the fundamental budget calculations?

The application period runs from 18 December 2025 to 15 April 2026 at 17.00 local time in Brussels. The budget stands at 21.00 million EUR, with three projects to be funded provisionally. It means that it is expected to contribute 7.00 million EUR to the project per EU.

What are the scientific and technical works that are within scope?

Consider process-to-prediction rather than model tuning. Typical ingredients:

 Observational data rescue, quality checking, and assimilation (local and historical data).

 Land-ocean-atmosphere Process-based improvements between observations, parameterizations, and assessment protocols.

 Extreme pushing methods and rainfall methods (convection, organization, biases, seasonal problems/generalization).

 Digital and HPC enablers AI/ML as an emulation, downscaling, automation of QC, and hybrid modelling: obviously justifiable and not black-box only.

Operationalisation of “preparedness” in Africa (interfaces with communities of national/regional services and climate services).

So what kind of consortium shape would score well in or about 7M EUR?

A balanced composition is recurrently desired: African universities/research institutes with actual leadership roles; African national meteorological/hydrological services or regional climate centers (relevance and uptake); European modelling centers/universities (methodology); HPC/data specialists (pipelines, scalability, reproducibility); and end-user organizations (risk management, agriculture, water, health). The exploitation and tech transfer are enhanced with the addition of innovative SMEs (data engineering, sensors, AI/ML tooling, digital platforms).

What shall we call Excellence that sounds generic yet?

Be cruel with measurability. Specify a chain of observations – process knowledge–model improvements-validated prediction ability of extremes/rainfall. Indicate the level of change in skill that is expected and its expected amount, what baselines you will compare with, and how the situation of African decision-making motivates your targets. Bring sparse/heterogeneous observations, convection biases, HPC constraints, and reproducibility to the fore–then bind them to go/no-go milestones and fallback options.

What does a professional Impact section entail about this subject?

Get Africa-specific pathway to impact: Who makes better predictions (through which agencies, to which sectors), when in their decision calendar, and by what channels? Assign outputs to adoption mechanisms: data formats, APIs, documentation, and training cohorts. Measure what is measurable: the number of datasets rescued and QC’d, the number of African early-career scientists trained, the number of operating interface pilots, the approximate percentage change in the chosen metrics of forecast skill, and a plan to transfer reproducible workflows.

Writing strategies that evaluators like?

Some of those which are consistent in assisting: parrot the topic language, without copy-pasting the text; have a one-page project logic map (what we have – how we do it – how we have made improvements – how we have validated our model – what we are making – how we are building capacity – what we are uptaking); be very specific on person-months and data management; and remind ourselves of the promise to have co-owners by making African partners lead projects (WP leads, data governance, publication strategy).

What can microfluidics and instrumentation contribute in this case?

Although climate modelling and data systems are the core of the area of interest, microfluidics would support the observation/QC pipeline. Examples include field-deployable lab-on-chip modules for denser networks (water quality proxies coupled to hydrology), standardized pre-processing (filtration, concentration, multiplexing) to minimize variability before assimilation, and even aerosol/cloud microphysics research under controlled conditions. Some microfluidic kits of training grade can be used to build local capacity in measurement and QA.

In what position does the Microfluidics Innovation Center (MIC) belong in a successful proposal?

MIC is a French SME focused on microfluidic engineering, instrument design, and the prototyping of scientific automation. We usually create rugged sensing/pretreatment modules, deploy them in data pipelines with strong QA, assist in implementing reproducible processes, and contribute to exploitation (industrialization roadmaps, tech transfer). Our success rates have historically been twice the official averages in Horizon Europe consortia, due to a combination of practical engineering, proposal-writing background, and R&D management. We have a routine of joining European consortia, and we feel at ease in the role of technical WP or cross-cutting (QA, data flows, validation setups).

How ought we to organize the budget and schedule pragmatically?

As a heuristic to a 7.00M EUR RIA in 36 months, most winning teams will allocate like one third to the data and observation work (rescue, QA, assimilation, infrastructure), one third to the modelling and methods work (including HPC), and one third to the capacity building, validation pilots, and uptake. Have reserves of HPC compute/storage and of maintenance field equipment. Bond significant expenditure on decision milestones to make the risk register financial.

Which evidence and KPIs do evaluators prefer to observe?

Statistical measures are good: number of new or reopened stations; terabytes of archived data recovered and checked; percentage decrease in critical biases; number of users flying services; number of African researchers in managerial positions; counts of training modules completed and retained capacity.

Will MIC assist with writing the proposal, or only with the hardware details?

Both. Our responsibilities include co-designing experimental setups, prototyping and testing microfluidic models, and making contributions to data and quality assurance pipelines. We also assist in developing the story, Excellence, Impact, and Implementation, and in making it meet the evaluators’ expectations. As experienced SMEs in European projects, we know how to turn technical depth into a comprehensible, fundable story.