Solar Software P2 Reference 4 min read Reviewed July 8, 2026 Nimesh Katariya Nimesh Katariya

Meteonorm

Meteonorm is the global TMY weather database used in PVsyst for solar energy yield modeling.

Definition

Meteonorm is a global weather database providing Typical Meteorological Year (TMY) data — GHI, DNI, DHI, temperature, wind speed — used by PVsyst and other solar simulation tools. Meteonorm 8 (2020) is the current bankable standard.

Key Takeaways

  • Meteonorm = global TMY weather database.
  • Used by PVsyst as default weather source.
  • Bankable for most projects; alternatives Solargis or NSRDB (US).
  • Commercial license required.
  • Latest version: Meteonorm 8 (2020).

How Meteonorm Fits Into a Yield Simulation

A PVsyst simulation is only as trustworthy as the weather data feeding it, and Meteonorm is where most projects get that data from by default. Instead of one year of measured on-site readings — which almost no project has — Meteonorm builds a synthetic Typical Meteorological Year (TMY) by blending long-term records from its global ground-station network with satellite and reanalysis data. The output is an hourly (or sub-hourly) file of GHI, DNI, DHI, ambient temperature, and wind speed that a designer imports directly into the simulation’s Site definition step, ready to run through the transposition, temperature, and loss models that produce the final P50/P90 numbers lenders review.

Worked Example

Take a rooftop commercial project in a region with no on-site pyranometer and no nearby first-class weather station. The designer opens PVsyst, selects the Meteonorm 8 source for that latitude/longitude, and the software interpolates a TMY from the nearest ground stations and satellite grid cells, weighted by distance and elevation. That TMY becomes the GHI/DNI/DHI input for the whole simulation. Because the site sits in a region with sparse ground coverage, the resulting uncertainty band is wider than it would be for, say, a European site near a dense network of stations — which is exactly the scenario where an engineer might cross-check the Meteonorm run against a Solargis dataset before finalizing the bankable report.

Weather data is only the input side of the equation — the value shows up once it runs through a simulation engine and produces a number a lender will actually sign off on. Our guide to advanced PVsyst analysis and how accurate yield predictions save millions walks through how weather-source choice affects P50/P90 outcomes, and the bankable PVsyst reports guide covers what lenders expect to see documented alongside the weather source. If you’re still comparing simulation platforms before committing to a weather database and workflow, the best solar design software roundup is a useful starting point, and SurgePV’s side-by-side solar design software comparison breaks down how different platforms handle weather-data integration in practice.

Frequently Asked Questions

6 commonly searched questions about Meteonorm.

What is Meteonorm?
Global weather database from Meteotest. Provides Typical Meteorological Year (TMY) data for solar energy modeling. Embedded in PVsyst as default weather source.
How accurate is Meteonorm?
Reasonable for most locations; uncertainty 5–10% for tropical and high-altitude. Solargis often more accurate for specific desert/tropical sites.
Is Meteonorm free?
No. Commercial product (€500–€2,500). Embedded in PVsyst at additional license cost. Solargis Prospect is a paid alternative.
Meteonorm vs. NSRDB?
Meteonorm: global. NSRDB: US-focused, free, satellite-derived. For US projects, NSRDB is preferred. For India and global, Meteonorm or Solargis.
Can Meteonorm data be used outside PVsyst?
Yes. Meteonorm can export TMY data as a standalone weather file (for example .epw or .csv) that other simulation tools, including SAM and some HOMER workflows, can import directly. PVsyst is simply the platform where it ships as the default embedded source.
How does Meteonorm generate data for a site with no nearby weather station?
Meteonorm interpolates from its global network of ground stations combined with satellite and reanalysis data, weighting by distance, elevation, and terrain. This is why accuracy varies by region — dense-station areas like parts of Europe are more reliable than remote desert or high-altitude sites, where Solargis's satellite-only approach can be a better cross-check.

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