I've spent the better part of my career surrounded by some of the sharpest minds in wind energy — first at Imperial College London, then for six years at the National Renewable Energy Laboratory in Colorado. At NREL, I had access to world-class computing resources, brilliant colleagues, and the freedom to push the boundaries of what we know about atmospheric flows, turbine dynamics, and extreme weather risk. It was, by every measure, a dream job for a researcher.
And yet, I kept running into the same uncomfortable realization.
The gap
The wind energy industry is making decisions worth hundreds of millions of dollars based on methods that are, in many cases, a generation behind the science. I don't say this to criticize — the industry has grown fast, and the consultancies serving it have had to scale even faster. But the consequence is a reliance on simplified models, conservative rules of thumb, and black-box commercial tools that nobody can audit.
Meanwhile, the research community has developed tools that are vastly more capable. OpenFAST can model the coupled aero-hydro-servo-elastic response of an offshore turbine in conditions that matter — not just the IEC design load cases, but the messy, site-specific realities of tropical cyclones, complex terrain, and turbulent wakes. FAST.Farm can simulate how an entire plant behaves dynamically, not just steady-state. WRF can downscale the atmosphere to the scales that actually drive turbine loads. These aren't exotic academic exercises — they are open-source, validated, and used at every major national lab.
But they rarely make it into commercial project work. The gap between what we know how to do and what projects actually get is enormous. And in offshore wind — where the stakes are highest, the conditions most extreme, and the margins thinnest — that gap is a problem.
Why it matters now
Offshore wind is scaling globally — and with that scale comes technical complexity that the current consulting ecosystem isn't set up to handle.
In Europe, the industry is mature but the challenges are intensifying. The UK's Auction Round 7 signals a renewed push for offshore capacity after the pricing difficulties of AR5, but the sites being developed now are further from shore, in deeper water, and in more complex metocean environments than the early North Sea projects. Developers can no longer rely on conservative assumptions borrowed from well-characterized sites like Dogger Bank. Each new lease area demands site-specific analysis that actually accounts for the local wind and wave climate — not a generic design envelope.
Meanwhile, emerging markets are facing challenges that existing frameworks were never designed for. Japan is pursuing offshore wind at scale in waters exposed to typhoons, complex bathymetry, and seismic activity — a combination that no European design standard was built to address. The Philippines, with some of the most typhoon-exposed coastlines on Earth, is beginning its offshore wind journey with essentially no local track record to draw from. In both cases, the technical questions are not incremental — they are fundamental. What are the design-driving loads for a 15 MW turbine in a Category 4 typhoon? How do you characterize a wind resource in a region where the extremes are governed by tropical cyclones, not extratropical storms? These aren't problems you can solve with a spreadsheet and a Weibull distribution.
Across all of these markets, lenders and investors are getting more sophisticated. They want to see analysis that is defensible, transparent, and reproducible. They want to understand the assumptions. They want to know that the tools have been validated and the person running them actually understands the physics — not just the software interface.
This is the moment where the research and the industry need to meet. And I realized that waiting for that to happen organically wasn't going to work.
What Parametrica is
Parametrica exists to close that gap. We bring the technical depth of a national lab to commercial project work — not by dumbing down the science, but by translating it into deliverables that lenders, independent engineers, and certification bodies can actually use.
Every analysis we produce is designed to be bankable. That means it has to be transparent (we use open-source tools, not black boxes), reproducible (our methods are documented and auditable), and defensible (grounded in validated physics, not just convention). We don't produce reports that sit on a shelf. We produce analysis that moves projects forward.
I also sit on the IEC 61400-3-1 TC88 working group — the committee responsible for the international design standard for offshore wind turbines. I mention this not to name-drop, but because it matters: when we advise on design loads, external conditions, or site-specific hazards, we do so with direct knowledge of how the standards are written and where they're headed. That context is hard to get from a textbook.
What this isn't
I want to be clear about something. Parametrica is not a large consultancy. We don't try to be everything to everyone. We focus on the technically demanding work that very few groups in the world can do — the kind of analysis where getting it wrong doesn't just cost money, it costs projects.
If you need someone to run a standard energy yield assessment using industry-standard commercial tools, there are excellent firms that do that. But if you need someone who can run a coupled OpenFAST simulation of your turbine in a Category 4 hurricane, downscale the atmospheric conditions with WRF, and tell you what that means for your design margins — that's where we come in.
Looking forward
Forming Parametrica was, honestly, a bet. A bet that the industry is ready for better science. A bet that developers, lenders, and engineers want transparency over convenience. A bet that the gap I saw from inside a national lab looks just as wide from the other side.
So far, the response tells me the bet was right.
If any of this resonates — whether you're a developer facing a tough site condition, an investor trying to understand the technical risk in a project, or an engineer who wants a second opinion from someone who actually runs the models — I'd love to hear from you.