PVsyst Meteo Data: Meteonorm vs Solargis vs NSRDB Compared

The meteorological dataset you feed into PVsyst shapes every number in your bankable yield report. Get the meteo source wrong and your P50 could be off by several percent, enough to push a marginal project’s DSCR below the lender threshold.

The three sources that matter, Meteonorm, Solargis, and NSRDB, each have specific strengths, geographic coverage profiles, and uncertainty characteristics that every yield simulation engineer must understand before selecting a reference dataset.

This guide provides a detailed comparison of all three sources plus NASA POWER, explains the Meteo Source Selection Matrix that engineers use to pick the right source, and gives geographic recommendations for India, the US, Africa, and Europe. For the broader bankable yield study workflow once the meteo source is selected, see the bankable PVsyst reports guide.

Quick answer. Meteonorm is embedded in PVsyst and is often acceptable where ground stations are close to the site. Solargis satellite data is widely used for DFI-financed projects in regions with few ground stations, and its cost is small next to the risk of an IE revision cycle. NSRDB is the usual primary source for US projects. NASA POWER is free but only suitable for pre-feasibility screening. Choosing poorly can move P90 by several percent, which at tight SECI tariffs can make a project hard to finance.

Why Meteo Source Choice Moves P90

Meteo source error is typically the largest single component in the yield report uncertainty budget, and getting it wrong is one of the ways accurate PVsyst yield predictions turn into costly revision cycles. A 5% GHI uncertainty from a low-quality dataset propagates to a P90 figure that is 5-7% lower than the true value, potentially making a marginal project unfinanceable.

Three mechanisms drive the divergence between sources: station density and interpolation radius; satellite era coverage (Solargis derives irradiance from METEOSAT imagery, capturing actual cloud dynamics rather than interpolating from a distant station); and data vintage, since an older averaging period can understate GHI relative to a recent satellite record where irradiance has trended upward.

Independent engineers reviewing yield reports routinely request meteo source documentation as an early check. A report that uses only one source, does not name the dataset version, or does not include a cross-validation table will receive a formal comment before the IE proceeds with detailed review.

Selecting sources with this in mind is often what separates a yield report that moves quickly through IE review from one that needs rounds of revision. PVsyst’s meteo documentation describes the sources it supports and how to import them.

Meteonorm: The Global Standard

Meteonorm is developed by Meteotest AG in Bern, Switzerland. It has been the default embedded meteo source in PVsyst since the early 2000s, and PVsyst licences include Meteonorm data access as a standard component.

How it works: Meteonorm interpolates measured data from a global network of approximately 8,000 ground stations. Where stations are sparse, it supplements with satellite data from multiple providers. It produces TMY (Typical Meteorological Year) data at hourly resolution, synthesised from a historical averaging period that depends on the version and the location; check the period stated in the dataset.

The TMY synthesis approach means Meteonorm data represents a “typical year” constructed from statistical analysis of the historical record, not an actual measured year. This is appropriate for long-term yield projection but differs from the time-series actual data provided by Solargis and NSRDB.

Geographic strengths: Meteonorm has excellent coverage in Europe, North America, and Australia where ground station density is high. The interpolation methodology is well-validated in these regions, and the results are consistent with independent ground measurements.

Geographic weaknesses: In sub-Saharan Africa, parts of South Asia, and remote areas globally, ground station density falls to one station per several hundred kilometres. In these regions, Meteonorm relies more heavily on satellite and modelled data, which carries higher uncertainty.

Stated uncertainty: Meteonorm quotes GHI uncertainty of ±4-5 % (one sigma) for locations near ground stations, rising to ±6-8 % in data-sparse regions.

PVsyst integration: Built in. Meteonorm data is embedded in PVsyst and accessible without additional subscription or data download. This convenience is why most PVsyst users default to Meteonorm: it is always available, requires no additional purchase, and the workflow is familiar. For many European projects, Meteonorm is genuinely the most accurate available source.

Where Meteonorm performs well: India’s major solar belts (Rajasthan, Gujarat, Andhra Pradesh) where IMD stations are reasonably dense and the nearest Class A station is under 60 km; Southern Europe, USA, and China where the station network is dense; and projects where the lender has not specified an alternative source.

Where Meteonorm underperforms: Sub-Saharan Africa where station gaps of 100-300 km are common; remote high-altitude sites in Himachal Pradesh or Ladakh; and coastal sites with persistent marine layer clouds that ground stations may not capture.

Solargis: The DFI Favourite

Solargis is a Slovak company that generates irradiance data from satellite observation, processed with a proprietary retrieval algorithm validated against ground measurement networks.

How it works: Solargis processes satellite imagery from METEOSAT (Europe, Africa, Middle East, India), GOES (Americas), and Himawari (East Asia/Pacific). The algorithm derives GHI, DNI, and DHI from satellite-measured reflected radiance, corrected for atmospheric aerosols, water vapour, and cloud properties. The dataset extends back to 1994 in most regions, providing 20-30 year historical records.

Unlike Meteonorm’s TMY synthesis, Solargis delivers actual hourly time-series data covering the full historical period. This allows calculation of true interannual variability (IAV) from the observed record, rather than relying on statistical synthesis.

Geographic strengths: Solargis is strongest in India, the Middle East, Africa and southern Europe: regions with high irradiance, low cloud cover, and where satellite-based observation outperforms sparse ground station networks. The METEOSAT satellite has excellent coverage of these regions with high temporal resolution.

Stated uncertainty: For India and southern Europe, Solargis quotes GHI uncertainty of ±3-4 % (one sigma). For sub-Saharan Africa, ±4-5 %. For high-latitude cloudy regions, uncertainty increases to ±6-8 %.

Cost: Solargis data is not included in the PVsyst licence. Site data costs from several hundred to a few thousand USD depending on product tier, historical period and location; get a current quote. The Solargis Prospect product provides quick-look data at lower cost for screening applications.

PVsyst integration: Solargis data is imported into PVsyst via the standard hourly data import (CSV format). The import workflow is straightforward but requires the engineer to purchase and download the dataset separately from the Solargis web platform.

Lender preference: Solargis is widely used on DFI-financed projects in India and Africa, and some term sheets name it or an equivalent satellite dataset. For projects targeting IFC or ADB financing in India, using Solargis can head off an IE comment requesting it. According to Solargis published validation data, its GHI uncertainty is in the ±3-5% range (1-sigma) against ground measurement.

Why DFI lenders prefer Solargis. DFI-financed projects (IFC, AfDB, USAID Power Africa) typically require an Independent Engineer review of the yield study. Many IEs cross-check yield reports against a satellite database. When the developer’s PVsyst report is based on Meteonorm and the IE’s cross-check differs by more than a few percent GHI, expect a resolution step: a ground measurement campaign or a rerun on satellite data. Starting with satellite data avoids that loop, which can cost weeks of schedule on DFI-financed projects.

NSRDB: The NREL-Backed Standard for the Americas

NSRDB (National Solar Radiation Database) is produced by NREL using the Physical Solar Model (PSM). It provides hourly irradiance data for the Americas (North, Central, and South America) and parts of Asia.

How it works: NSRDB uses GOES satellite data processed with the PSM algorithm to generate half-hourly GHI, DNI, and DHI values at 4 km resolution across the covered region. The CONUS (continental US) coverage is the most dense and validated portion of the dataset, with high consistency with ground measurements from the SURFRAD and ARM (Atmospheric Radiation Measurement) networks.

The NSRDB PSM v3 dataset, the current standard, provides data from 1998 to the present, with regular updates. This 25+ year record supports a solid IAV calculation for US projects without requiring a separate long-series data purchase.

Geographic strengths: CONUS coverage is the strongest of any public dataset for US projects, with high spatial resolution and extensive ground validation. Coverage extends to Canada, Mexico, Central America, South America, and parts of Southeast Asia and South Asia with varying accuracy.

Stated uncertainty: For CONUS, NSRDB PSM v3 quotes GHI uncertainty of ±3-5 %. For outside-CONUS locations, uncertainty increases to ±5-8 %, with higher uncertainty in regions where GOES satellite coverage is less optimal.

Cost: Free. NSRDB is a public dataset available via the NREL API. SAM integrates NSRDB natively; PVsyst requires a CSV export from the NREL data viewer and import via the hourly data import function.

PVsyst integration: NSRDB data must be exported from the NREL API (or the SAM/NSRDB web interface) and imported into PVsyst via the hourly data import function. This adds a workflow step relative to Meteonorm but is straightforward for engineers familiar with the NREL data tools. The resulting PVsyst simulation is fully equivalent to one using Meteonorm data.

Lender acceptance: For US projects, NSRDB is widely accepted by domestic lenders and IE firms. For projects outside the Americas, NSRDB coverage is limited and the dataset is not the preferred choice for most IEs working on Indian or African projects.

The database uses the Physical Solar Model (PSM v3.x) applied to GOES satellite imagery, producing 30-minute resolution data from 1998 to 2023. According to NREL’s NSRDB documentation, PSM has been validated against SURFRAD and other ground networks, with low GHI bias for most continental US locations.

NASA POWER: The Free Global Fallback

NASA POWER (Prediction of Worldwide Energy Resources) is a free web service providing meteorological and solar data derived from NASA satellite instruments. For PVsyst users, it can produce a TMY file for any location on Earth with approximately 0.5° × 0.5° spatial resolution (roughly 55 km grid cells).

When NASA POWER is appropriate. Pre-feasibility screening for sites in data-sparse regions of Africa or Southeast Asia where neither Meteonorm nor Solargis data has been purchased. Cross-check sanity check: if NASA POWER GHI deviates from Meteonorm by more than 7%, that is a flag to investigate the data sources.

When NASA POWER is not appropriate. Any submission to a lender, DFI, or Independent Engineer. Any project financed above $5M equivalent. Its coarse grid and higher uncertainty are too large for bankable energy yield studies.

Africa-Specific Considerations for Meteo Source Selection

For Tunde and other African EPC teams, the meteo source decision is more consequential than it is in India.

Africa presents three structural challenges: station scarcity (large portions of the Sahel and East Africa have no WMO Class A radiation stations within 200 km of major solar zones); aerosol loading and harmattan dust that can reduce GHI by 15-25% during affected months (which Solargis handles better via MODIS aerosol data); and DFI lender expectations (IEs on DFI-financed African projects commonly cross-check against satellite data such as Solargis).

The practical recommendation for African DFI projects: purchase Solargis TMY data before preparing the bankable PVsyst report. The lenders due diligence engineering guide covers the full IE review process once the meteo source is confirmed.

The Meteo Source Selection Matrix

The Meteo Source Selection Matrix is Heaven Designs’ four-axis framework for selecting the correct irradiance database before opening PVsyst. It maps geography, data vintage, lender acceptance, and cost to a single recommended source, and flags when a secondary cross-check is mandatory.

Head-to-Head Accuracy Comparison

Characteristic	Meteonorm	Solargis	NSRDB
Data source type	Station interpolation + ERA5	Satellite primary	Satellite (GOES)
GHI uncertainty, CONUS	±4-5 %	±4-5 %	±3-4 %
GHI uncertainty, India	±5-7 %	±3-4 %	±5-6 %
GHI uncertainty, sub-Saharan Africa	±6-8 %	±4-5 %	Not covered / high uncertainty
GHI uncertainty, Europe	±4-5 %	±3-5 %	Not covered
Historical period	Varies (1996-2015 typical)	1994 - present	1998 - present
Temporal resolution	Synthetic hourly TMY	Hourly (actual time series)	30-min (actual time series)
Spatial resolution	Interpolated to site	3-5 km	4 km
Cost	Included in PVsyst	Several hundred USD and up, per site	Free
PVsyst integration	Native (built in)	CSV import	CSV import
DNI derivation	Decomposition from GHI	Direct satellite measurement	Direct satellite measurement

The Meteo Triangulation Standard: Why Dual-Source Matters

Best practice for bankable yield reports is to use at least two independent meteo sources and document the comparison.

The triangulation serves two purposes:

Detecting localised bias: If Solargis and Meteonorm agree within 2 %, confidence in the reference GHI is high. If they diverge by 4 %+, the engineer must investigate the source of disagreement before selecting a reference dataset. Large divergences typically point to localised aerosol loading, terrain effects, or historical period differences.

Reducing source uncertainty: Using a weighted average of two sources reduces the data source uncertainty component in the overall uncertainty budget, raising the P90 value and improving project bankability. A triangulated source uncertainty of ±2-3 % compares favourably to the ±5-7 % that would be assigned for single-source Meteonorm in data-sparse regions.

A typical triangulation result for an Indian utility-scale site in Rajasthan:

Source	Annual GHI (kWh/m²/year)	Relative to Average
Solargis	1,842	+1.1 %
Meteonorm	1,802	−1.1 %
Average (reference)	1,822	,
Source uncertainty	±2.2 %	(half the inter-source spread)

This 2.2 % source uncertainty compares favourably to the ±5-7 % that would be assigned for single-source Meteonorm in Rajasthan. The improvement is worth approximately 200-400 MWh of additional bankable P90 production on a 50 MW project, a meaningful financial impact.

The Heaven Designs Meteo Source Selection Protocol, The 4-Step Site Assessment

Heaven Designs follows a structured four-step protocol to select the appropriate meteo source(s) for each project. This protocol is applied before any PVsyst simulation begins.

Step 1, Geographic Classification We classify the site into one of four zones: CONUS (US), India/South Asia, sub-Saharan Africa, or Rest of World. Each zone has a default primary source: NSRDB for CONUS, Solargis for India and Africa, Meteonorm for Rest of World.

Step 2, Long-Series Availability Check We check whether the site has 20+ years of historical data from the primary source. Long-series data reduces the uncertainty of the long-term mean roughly in proportion to 1/√n, where n is the number of years. If only 10-year data is available, we note the IAV penalty in the uncertainty budget and investigate whether an extended Solargis or NSRDB dataset can be purchased.

Step 3, Secondary Source Acquisition We always acquire a second source for bankable reports. For Indian sites: Solargis primary + Meteonorm secondary (or NSRDB if the Indian coverage is adequate). For CONUS sites: NSRDB primary + Meteonorm secondary. For African sites: Solargis primary + Meteonorm secondary. The secondary source does not need to be the same quality as the primary; it serves as a cross-check and reduces the statistical uncertainty associated with single-source reliance.

Step 4, Comparison and Reference Selection We run both datasets through PVsyst and compare annual P50 yields. If the difference is within ±3 %, we use the weighted average as the reference with the inter-source spread as the source uncertainty. If the difference exceeds 3 %, we investigate using NASA POWER, ERA5, or any available ground measurement data before selecting a reference. A decision memo documenting the source selection rationale is included in the report appendix.

Regional Recommendations

India: Use Solargis as primary. The METEOSAT satellite processing gives Solargis a clear accuracy advantage over Meteonorm in peninsular India, Rajasthan, and Gujarat. Indian lenders generally accept Solargis, and IFC and ADB financing often expects it. Cross-validate with Meteonorm, or with the free sources in our India irradiance data guide. The Solargis Prospect quick-look product is sufficient for feasibility screening; the full hourly dataset is required for bankable simulation.

Continental US: Use NSRDB as primary. The GOES satellite processing and multi-station ground validation make NSRDB the best-available US dataset. Cross-validate with Meteonorm. Solargis is also acceptable for US projects and may be preferred by European-headquartered IEs reviewing US projects, but it adds cost without material accuracy improvement over NSRDB for CONUS locations.

Sub-Saharan Africa: Use Solargis as primary. Ground station density across most of Africa makes Meteonorm interpolation unreliable. Solargis METEOSAT processing has been validated across multiple African utility-scale projects and is preferred by IFC, AFDB, and DEG. Cross-validate with Meteonorm but weight Solargis more heavily in the reference selection.

Europe: Meteonorm and Solargis are both excellent. Either is acceptable as primary. Cross-validate with the other. For southern Europe (Spain, Italy, Greece, Portugal) with high irradiance and good METEOSAT coverage, Solargis is slightly preferred. For northern Europe and the UK where cloud cover is variable, Meteonorm’s station-interpolation approach may be more reliable.

Middle East and North Africa: Solargis primary. High irradiance, excellent METEOSAT coverage, limited ground stations, and a strong Solargis validation track record in this region.

Connecting Meteo Choice to the Broader Yield Report

The meteo source choice feeds directly into the uncertainty budget documented in P50/P90 yield reports. A well-chosen dual-source approach can reduce the data source uncertainty component by 1-3 percentage points, improving P90 yield and strengthening project bankability.

For projects using PVsyst tracker yield studies, meteo source accuracy is doubly important because tracker gain is sensitive to the DNI/DHI decomposition in the dataset. Solargis and NSRDB both provide direct DNI measurement from satellite observation, while Meteonorm derives DNI from GHI using decomposition models: an additional uncertainty source for tracker projects that can contribute 0.5-1 % error in tracker-vs-fixed-tilt gain estimates.

For the full PVsyst simulation workflow, including meteo source import, loss parameterisation and report output, see our guide to PVsyst simulation outsourcing. Our site survey and feasibility service can include a dual-source meteo evaluation.

For developers preparing for IE review, our guide on validating a PVsyst report before lender submission covers the complete pre-submission checklist, including the specific meteo documentation an IE reviewer will look for.

Key Numbers

FAQ

Which meteo source does PVsyst include by default? PVsyst licences include embedded Meteonorm data. Solargis and NSRDB require separate data acquisition and CSV import into PVsyst. This is why Meteonorm is the default choice for many engineers: it requires no additional steps or purchases.

Is Solargis worth the cost for Indian projects? Yes. For Indian utility-scale projects targeting institutional finance (IREDA, SBI, IFC, ADB), Solargis is commonly the IE-preferred source. Its cost is small relative to project size and the risk of IE comments on meteo documentation.

Can I use NASA POWER as a free alternative to Solargis? NASA POWER (from MERRA-2 reanalysis) is an acceptable cross-check source but is not accepted as a primary source by most IEs. Its resolution (0.5° × 0.625° lat/long) is too coarse for site-specific yield simulation, and its stated GHI uncertainty is higher than Solargis or NSRDB. Use it as a third cross-check when Solargis and Meteonorm diverge significantly.

Why do Meteonorm and Solargis sometimes disagree by more than 5 %? Large disagreements typically arise from localised aerosol loading (dust, industrial pollution), complex terrain (mountain valleys, coastal fog zones), or differences in the historical averaging period. When disagreement exceeds 3 %, triangulating with a third source (NASA POWER or ERA5) helps identify which source is more reliable. Document the investigation in the report.

Does the meteo source affect tracker vs. fixed-tilt yield differently? Yes. Tracker yield is more sensitive to the DNI/DHI decomposition in the dataset because diffuse irradiance does not benefit from tracking. Datasets that overestimate DNI will overstate tracker gain. Solargis and NSRDB provide direct DNI observation from satellite processing; Meteonorm derives DNI from GHI decomposition, which adds uncertainty for tracker projects in the range of 0.5-1 % of yield.

What does Heaven Designs use for African projects? For sub-Saharan African projects, we use Solargis as primary and Meteonorm as secondary cross-check. For projects targeting DFI financing (IFC, AFDB, DEG), we include a Solargis validation summary in the report package to satisfy IE requirements without the IE needing to request it separately.

How does meteo source choice affect P90? A lower-quality meteo source (higher uncertainty sigma) produces a lower P90 for the same P50. Switching from single-source Meteonorm (±6 % uncertainty in India) to dual-source Solargis+Meteonorm (±3 % combined) can raise P90 by 2-4 % of annual energy, potentially worth hundreds of thousands of dollars in additional debt capacity on a large project.

Is NSRDB reliable for Indian projects? NSRDB coverage for India exists in the dataset, but accuracy is lower than for CONUS: typically ±5-6 % rather than ±3-4 %. NSRDB can serve as a useful secondary cross-check for Indian projects but should not be used as the primary source where Solargis is available.