I started Parametrica for the early, hard part of a renewable-energy project — the risks and design decisions that get locked in long before there is any trustworthy data available to inform them. That is the work I care about most, and after a decade of doing it inside a university and a national lab, I wanted to put it directly in front of the people making those calls — and to build the firm I wished the industry already had.
I've spent most of my career surrounded by some of the sharpest minds in wind energy — first at Imperial College London, and then at the National Renewable Energy Laboratory in Colorado. World-class computing, brilliant colleagues, and the freedom to push what we know about metocean, wind turbine dynamics, and how assets behave under extreme conditions. A dream job for a researcher — and yet I kept running into the same uncomfortable realisation.
The gap I kept seeing
Renewable energy runs on engineering models — standardised, fast, and good enough for the routine case. What it rarely uses are high-fidelity models: physics resolved finely enough to show what actually happens to an asset in the conditions that decide a project, such as how a wind farm behaves when a typhoon passes over it. These models exist, and they work. Our turbulence-resolving simulation of Hurricane Laura reproduced the storm's extreme winds and eyewall turbulence directly from the physics. [1] The trouble is that this kind of modelling almost never reaches a live project, and least of all early — when the design is still on paper and the decision can still change.
That becomes a particular risk with new technologies, immature designs, and extreme-weather-prone sites, decided while the project is still on the drawing board and every assumption is about to be cast in steel. Get those decisions right and everything downstream — certification, financing, insurance, twenty years of operation — gets easier. Get them wrong and no amount of later analysis buys it back. The industry has learned to live with that gap; Parametrica exists because it shouldn't have to.
Where we go deep
So that is where Parametrica works. Our strength — and the reason clients call — is identifying the risks and highlighting the design decisions that matter before they are locked in, and backing them with in-depth engineering tailored to the specific asset and site, not a fleet average. Getting that layer into a project early is as much a workflow problem as a physics one, which is why we build it around TurbineX, a solver-agnostic platform that folds engineering-tier and high-fidelity models into the design loop without tying a client to any one code. For a developer, that means a better design and a defensible bid; for an insurer or broker, a number built from the physics up — one that survives an independent engineer and a renewal.
It is the same physics at both ends of an asset's life. The aeroelastic model that defends a turbine in front of a certifier should also describe what that turbine actually does — component by component — over twenty years in the field. Almost nobody runs it that way. They should.
Risk engineering first, parametric models next
That in-depth engineering is the foundation everything at Parametrica is built on — and it is what lets us build parametric risk models on a basis you can defend: Caerus, physics-based triggers for renewable-energy assets under extreme-weather or operational conditions, keeping basis risk — the gap between what a trigger pays and what a client actually loses — as small as the physics allows. For too long, this risk has been priced on borrowed numbers and inherited convention. Parametrica is built to change that — to give renewable-energy risk engineering a physics-first foundation, and to pioneer the parametric models that follow.
An invitation
Forming Parametrica was a bet — that the industry is ready for better physics, and that developers, insurers and engineers want transparency over convenience. So far, the response says it was.
If any of this resonates — a developer facing a tough site or a first-of-a-kind design, an insurer or broker who needs more than a single vulnerability curve for a large exposure, or a team that wants a second opinion from someone who actually runs the models — I'd like to hear from you. Engineering intelligence for resilient energy systems isn't a tagline; it's the whole reason I started.
References
[1] Sanchez-Gomez, M., Deskos, G., Lundquist, J. K. (2025). Turbulence-resolving simulations of Hurricane Laura (2020): Insights into extreme winds and eyewall turbulence. Quarterly Journal of the Royal Meteorological Society, 151(773), e70003. 10.1002/qj.70003