OUR SCIENCE

See where every number comes from.

Every P50, IRR or hardening plan we hand over is built layer by layer, from decades of observed weather and the full range of climate simulations down to the components of each asset.

HOW IT WORKS

From climate signals to financial decisions.

A climate signal is not a decision. Most climate models work top-down - global projections, regional averages, site-level estimates. We work in reverse: we start from local ground truth and build up, layer by layer, until the output fits the decision an investment committee, a regulator or an operations team needs to make.

01 / 05

Terrain · Elevation · Microclimate

Climate and topography layer

We start from local ground truth: the actual terrain, elevation and microclimate of each asset location. The result is not a regional hazard score - it's climate behaviour specific to your exact coordinates. In complex terrain, coastal zones and valley floors, the difference between these two approaches is where the financial risk hides.

02 / 05

Locations · Components · Thresholds

Asset layer

A wind turbine blade, a high-voltage transformer and an overhead conductor fail at different conditions - and most risk models treat them identically. We model the engineering threshold of each asset, with the option to take it down to component level: the temperature at which solar panels derate, the wind speed at which turbines shut down, the moisture level at which substation insulation degrades. Two assets on the same site can carry very different exposure once you model at that level.

03 / 05

Dependencies · Networks · Co-location

Cascading risk layer

Assets don't fail in isolation. A wind farm and a co-located battery share more than a grid connection - they share the same weather exposure, so a bad year hits both at once. Risk also travels through the network: a substation failure cascades to every asset downstream. We model these dependencies, so you see the systemic risk that single-asset analysis misses.

CLIMATE DATA

Where the data comes from

CMIP6 model ensemble: 400+ global climate simulations. Not cherry-picked 3-4 scenarios - the full ensemble with P10 to P90 uncertainty ranges. Because the spread between optimistic and pessimistic projections is where the financial risk sits.
CORDEX-CORE + ML downscaling: Regional climate projections at 11-30 km resolution, refined through machine learning to match each asset's elevation, terrain and microclimate. Global models show the trend; downscaling shows what happens at your exact location.
ERA5 reanalysis: Historical climate baseline from the European Centre for Medium-Range Weather Forecasts. 40+ years of hourly atmospheric data to calibrate forward projections.
Fathom global flood model v1.3: 30-metre resolution flood modelling: river floods, flash floods, surface water. The same data insurers and governments use for flood risk assessment.

THE DETAILS

What goes into the numbers

1
Climate signals
10+ physical hazards modelled at asset location across SSP1-2.6, SSP2-4.5 and SSP5-8.5. 400+ CMIP6 simulations with P10-P90 uncertainty ranges. Coverage from 1950 to 2100.
2
Operational impact
Component-level vulnerability modelling. Solar panels derate from 40°C, with a cliff at 55°C (-60%). Wind turbines shut down above 40°C, below -20°C, or at gusts above 52.5 m/s. CapEx damage (average annual loss) and OpEx impact (yield loss, revenue reduction, downtime) per asset.
3
Adaptation and pricing
Concrete adaptation CapEx options with payback periods, insurance overlay modelling, and the net residual position after mitigation.
4
Financial decision
Climate-adjusted IRR for investors, yield and revenue outlooks for renewable operators, SAIDI reduction and avoided outage costs for grid companies. AI-generated summaries explain the results in the language your board or investment committee expects.

VALIDATION

Tested with real portfolios

Wind and PV locations assessed globally with a leading renewable energy operator. Validated gross-to-net framework showing up to -5% gross productivity loss by 2060 before adaptation.

€1.26B

Grid infrastructure portfolio assessed: 18 substations and 58,850 km of overhead lines across 3 countries. CapEx exposure quantified under RCP2.6 and RCP8.5 to 2050.

P10-P90

Full uncertainty ranges, not just the median projection, because "average climate change" doesn't describe what hits your asset.

SCENARIOS

Three futures. Your portfolio in each.

SSP1-2.6

Climate protection

Global warming limited to ~1.8°C by 2100. Strong mitigation policies. Even here, physical impacts on infrastructure are significant.

SSP2-4.5

Middle of the road

~2.7°C warming. Current policy trajectory. The most likely pathway for infrastructure with 20-30 year hold periods.

SSP5-8.5

High emissions

~4.4°C warming. The worst-case stress test, essential for understanding tail risk and designing resilient adaptation.

SCIENTIFIC HERITAGE

Where our science comes from

Repath is a spin-off of the University of Hamburg, founded by climate scientists and engineers. Our team includes scientists from the Climate Service Center Germany (GERICS), the Potsdam Institute for Climate Impact Research (PIK), the Max Planck Institute for Meteorology (MPI-M) and the University of Potsdam.

That's why we don't simplify climate science for convenience. We run the full model ensemble, combined with satellite observations, machine learning downscaling and AI-generated analysis: the scientific standard used by reinsurers and the IPCC, translated for infrastructure decisions.

See the science applied to your sites.

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It starts with a 30-minute intro call about your question.