Every solar project—from a 100 kW rooftop to a 5 GW utility-scale portfolio—depends on solar resource data. The smartest teams in the industry don’t just trust that irradiance data; they scrutinize its accuracy and account for its uncertainty at every step.
With the accelerated phase-out of the ITC in the U.S. and tightening operating margins, every advantage solar developers can bring to their financing pays dividends, and it starts with the data. Get it right, and your energy estimates hold up and financing terms stay competitive. Get it wrong, and errors compound through P50/P90 estimates, DSCR, IRR and eventually, real revenue.
“Why would I pay for solar data when I can get it for free?”
It’s the single most common question we hear—and it’s a fair one. Datasets like the National Solar Radiation Database (NSRDB) are valuable public resources, and for early-stage exploration, they’re often a great starting point. But when a project moves toward development, financing or operations, the differences between a free dataset and one that’s continuously updated and independently validated show up directly in your energy estimates, financing terms and returns.
In this article, we illustrate the value of using a commercial solar resource dataset such as SolarAnywhere® by sharing the results of a comparison between SolarAnywhere and NSRDB data. The results reveal an accuracy gap at every timescale, and how that gap can negatively impact the accuracy of your energy projections and project ROI. This isn’t the full story, however.
There are many additional advantages of using SolarAnywhere, some of which we summarize below.
Example Financing Scenario
See what lower uncertainty buys you in terms of dollars: more debt capacity, better DSCR headroom and higher equity IRR.
Proven, transparent modeling
- Peer-reviewed model – SolarAnywhere was built on the SUNY satellite-to-irradiance model (Dr. Richard Perez, University at Albany). It’s one of the most widely cited and independently scrutinized irradiance models in the industry, and we’ve been refining it over two decades.
- One globally consistent model – The SolarAnywhere model is consistent across GOES, Meteosat and Himawari, so a project in Chile or Vietnam is modeled with the same rigor as one in Texas, regardless of whether there are nearby ground measurements.
- Documented, versioned model updates – Each model update is paired with a published validation whitepaper, so you always know which model produced your data and how it performed.
- Transparent validation – We publish our validation methodology and formulas so any customer, independent engineer or financial partner can reproduce our results.
- Dedicated research team – The SolarAnywhere model is backed by an in-house research team that continuously improves the model and supports customers directly.
Broader coverage with SolarAnywhere
- 1998 to present – A continuously reprocessed, 25+ year historical record.
- Present to +14 days – Forecast data for operations, dispatch and short-term planning.
- 2015 to 2099 – Downscaled climate projections for long-term asset planning and resilience studies.
- Global, high-resolution data – Data resolutions down to 1 km and 500 m spatial resolution, and 1-minute time series to support the detail advanced engineering teams need.
A fully staffed technical and account management team
- Support and account teams – Located in Bellevue, Wash., and Berlin, Germany, with live chat during business hours.
- Detailed API documentation – Documentation includes example requests and code samples to streamline your workflows.
- Information when you need it – Available 24/7 via a fully maintained Support Center covering models, validation and integration partnerships.
- Easy-to-use Data Portal – From the Data Portal, purchase and access datasets, and get free sample datasets at select locations around the world to evaluate before you buy.
But the real answer is accuracy
Time coverage, tooling and support matter. And yet, the core reason customers choose SolarAnywhere is straightforward: lower bias and uncertainty, held consistently across climate zones and across the full historical record.
The benchmark: How we compared SolarAnywhere V4.1 to NSRDB
Every dataset carries some bias and uncertainty, and choosing one means accepting those tradeoffs—often without realizing how far they propagate into energy yields, financing terms and operations. We ran this benchmark to help quantify those tradeoffs, so the choice is a deliberate one, not an accidental one.
To measure the accuracy difference between SolarAnywhere V4.1 (released in May 2026) and NSRDB PSM v4.0.0 (released in May 2023), we benchmarked both datasets against independent ground measurements. The setup, scope and methodology are outlined below.
- Ground reference network – 19 publicly accessible pyranometer stations across SOLRAD (NOAA), SURFRAD (NOAA), NLR and SRML (University of Oregon). These high-quality reference stations are predominantly equipped with secondary-standard / Class A (ISO 9060) thermopile pyranometers, the reference-grade instruments used in peer-reviewed, satellite-model validation studies.
- Scope – North America, measuring annual, monthly and daily accuracy, specifically relative Mean Bias Error (rMBE), Standard Deviation of rMBE (Std), relative Mean Absolute Error (rMAE) and relative Root Mean Square Error (rRMSE).
- Methodology – For every station, we pulled coincident hourly time-series data from SolarAnywhere V4.1 and NSRDB, then computed rMBE, Std, rMAE and rRMSE using identical formulas for both datasets to ensure no methodological asymmetry. Results were evaluated per site, per year and aggregated across the network. For full detail on data filtering, quality control and exact equation definitions, see our V4.1 validation whitepaper.
SolarAnywhere V4.1 vs. NSRDB
Figures 1 through 3 show how SolarAnywhere V4.1 compares to NSRDB across common validation metrics, evaluated over the same reference sites and time periods.
| Metric | NSRDB | SolarAnywhere V4.1 | SolarAnywhere Improvement |
|---|---|---|---|
| Annual rMBE | 1.40% | 0.35% | ~75% lower bias |
| Standard Deviation of rMBE (Uncertainty) | 2.80% | 1.53% | ~45% lower |
| Annual rMAE | 2.36% | 1.23% | ~48% lower |
| Annual rRMSE | 18.25% | 14.15% | ~22% lower |
| Metric | NSRDB | SolarAnywhere V4.1 | SolarAnywhere Improvement |
|---|---|---|---|
| Annual rMBE | 4.30% | 1.38% | ~68% lower bias |
| Standard Deviation of rMBE (Uncertainty) | 5.52% | 3.64% | ~34% lower |
| Annual rMAE | 5.78% | 3.10% | ~46% lower |
| Annual rRMSE | 35.98% | 26.17% | ~27% lower |
| Timescale | Metric | NSRDB | SolarAnywhere V4.1 | SolarAnywhere Improvement |
|---|---|---|---|---|
| Hourly GHI | rMBE | 1.40% | 0.45% | ~68% lower bias |
| Hourly GHI | rRMSE | 18.02% | 14.00% | ~22% lower |
| Monthly GHI | rMBE | 1.00% | 0.82% | ~17% lower bias |
| Monthly GHI | rRMSE | 19.64% | 15.04% | ~23% lower |
| Daily GHI | rMBE | 2.43% | 1.98% | ~19% lower bias |
| Daily GHI | rRMSE | 21.69% | 16.48% | ~24% lower |
What the per-site data shows
Looking at the per-site, per-year heatmaps in Figures 4 and 5, SolarAnywhere’s residuals remain closer to zero and more consistent year-over-year than NSRDB (hover over the heatmap to see specific values). NSRDB shows persistent, site-specific biases (e.g., SurfradPennState, SurfradGoodwinCreek, SurfradBoulder, SurfradBondville) that stay embedded across many years.
This is the kind of hidden structural error that quietly corrupts long-term energy estimates.
The historical accuracy problem: Why older NSRDB data drifts
There’s a nuance that most data users miss, and it’s one of the most important differences between SolarAnywhere and NSRDB.
Satellite platforms don’t stay static. Sensors drift over time, calibration standards evolve and every 10 to 15 years, an entirely new generation of geostationary satellite comes online. Over the Americas, GOES has moved from GOES-8 (1994) to GOES-13 (2006) to the current GOES-R series (GOES-16 as GOES-East in late 2017), with GeoXO to follow in the early 2030s.
The same pattern holds worldwide. Over Europe and Africa, Meteosat’s first generation ran from 1977 to 2017, giving way to Meteosat Second Generation (12 spectral channels, up from the original 3) and now Meteosat Third Generation, operational since 2024. Over Asia-Pacific, Himawari made a comparable leap from Himawari-7 to the far more capable Himawari-8/9.
Every one of these transitions changes the underlying measurement inputs, and a globally consistent historical record must actively correct for all of them.
How satellite changes are handled in SolarAnywhere
With every SolarAnywhere annual model release, we reprocess the full historical record—not just recent years. Each reprocessing incorporates new ground validation data, updated platform-specific calibration and corrections for known sensor drift. This means accuracy doesn’t decay in older satellite eras.
Our 2005 data is held to the same standard as our 2025 data, and annual accuracy stays flat across the entire record.
What the benchmark shows
In our benchmark, NSRDB’s annual GHI bias grows in earlier years, while SolarAnywhere’s stays flat across the full record. As shown in Figure 5, deviation in the NSRDB dataset increases materially in the years before the GOES-R series became operational in late 2017. This reflects the coarser resolution and different calibration of the earlier GOES satellites, and illustrates how this is an input-data challenge that must be actively corrected during reprocessing.
Our benchmark suggests SolarAnywhere does so more consistently across satellite eras, keeping accuracy stable all the way back through the historical record.
Why this matters
Most solar projects use 20+ years of historical irradiance data to build a long-term average and estimate P50/P90 energy. If the older years in your dataset carry a hidden bias, your entire long-term average is skewed, and no amount of “current year” accuracy fixes it. You’re financing a 25-year asset on top of a 20-year historical record that isn’t consistent with itself.
Consistency across time is as important as accuracy in any single year.
Why trusted data matters at every project scale
Data accuracy matters most at utility scale as the projects are enormous, and a small resource error moves millions of dollars in revenue. But it would be a mistake to conclude that smaller projects can safely ignore it. The dollars are smaller, but so are the margins and the cushion.
Either way, the cost of getting it wrong lands on the owner or financier every year the project operates. Against that exposure, the cost of better data is trivial.
For distributed generation (DG) and C&I projects
Small projects have less room for error. When the data is off, it’s the system owner who feels it. This is because the decision to build, and the returns they expect, are both based on that data.
Here’s what a small error costs. Say a 500 kW rooftop is expected to produce 700,000 kWh a year. If the data overestimates solar insolation by 3%, the system will actually produce 21,000 kWh less than promised every year. At the current U.S. commercial electricity average of 13.5¢/kWh, that’s roughly $2,800 a year (or approximately $71,000 over the system’s 25-year life) that the owner expects but never gets. On a larger 2 MW system or portfolio, the same 3% error adds up to approximately $11,300 a year, or roughly $285,000 over its life.
In this example, the PV systems run fine, they just make less money than the plan said they would based on using higher-uncertainty data. That means lower returns for the owner, possible shortfall payments if production was guaranteed and more cautious pricing on the next deal.
Compared to the losses in this example, more accurate data pays for itself many times over. It’s also worth noting that small projects don’t need the most detailed, high-resolution datasets. They rarely need the hour-by-hour or sub-hourly (Time-Series) data that big utility-scale projects use for detailed energy yield assessments. Simple monthly irradiance totals and lower resolution Typical-Year files give you the same trusted SolarAnywhere accuracy, at a price built for smaller projects.
For utility-scale energy yield assessment and project financing
At utility scale, the math is unambiguous. A single point of bias on a 200 MW project can swing millions in lifetime revenue and reshape how much debt a lender will size. Every basis point of resource uncertainty translates directly into debt capacity, DSCR headroom and equity IRR. We break this down in detail in the next section.
For O&M and asset management
Once a project is operating, resource data drives performance benchmarking, performance-guarantee verification and warranty claims. When a plant looks like it’s underperforming, the first question is why. Is it the panels, the inverters, the O&M provider or the data? Because metrics such as Performance Ratio, are calculated by dividing actual energy by the expected energy from measured irradiance, biased resource data can make a healthy plant look deficient—or mask a real problem.
Most plants use on-site MET stations for performance benchmarking, but it’s an imperfect solution. Ground sensors soil, drift, lose calibration and go offline. Across 14 sites, BayWa r.e. documented ~$59,000 to restore neglected MET stations plus ~$40,000/year in maintenance and gap-filling.
Satellite data doesn’t have these problems, and some operators now use it as their primary feed. For example, BayWa replaced on-site MET stations with real-time SolarAnywhere data, finding the difference versus a calibrated station “equal to or less than the measurement accuracy of the weather stations themselves.” Others keep it as a high-quality backup, such as Invenergy Services, which uses SolarAnywhere to fill gaps in its ground data to keep benchmarking consistent.
Either way, accurate and trusted resource data can pay for itself in a single avoided dispute or retired MET station.
What lower uncertainty actually buys you: A financing scenario
Let’s ground this in dollars.
Figure 7 considers a 200 MW utility-scale project with a P50 energy estimate of ~438,000 MWh/year (an approximate 25% capacity factor) and a merchant + PPA blended revenue of approximately $29/MWh. Assume identical everything—same panels, same site and same capital structure—except the resource dataset’s uncertainty. One project is financed on NSRDB uncertainty; the other on SolarAnywhere V4.1 uncertainty.
Figure 7: Financial Comparison Using NSRDB vs. SolarAnywhere Data
200 MW Utility Scale Project
| Assumption | NSRDB Case | SolarAnywhere V4.1 Case |
|---|---|---|
| Resource-data uncertainty (1σ std of bias) | 2.80% | 1.53% |
| Other components, combined (IAV, transposition, soiling, power model) — identical | ~4.56% | ~4.56% |
| Total P50 resource uncertainty (1σ, all sources) | ~5.35% | ~4.81% |
| Resulting P90/P50 ratio | ~0.931 | ~0.938 |
| P90 annual energy (MWh) | ~408,000 | ~411,000 |
| P90 annual revenue | ~$11.8M | ~$11.9M |
| Additional P90 revenue | Baseline | ~$88K/year |
| Lender-sized debt (at 1.35x DSCR on P90 CFADS) | Baseline | higher debt capacity |
| Effective DSCR at P50 | ~1.50x | ~1.52x |
| Equity IRR (at same debt terms) | Baseline | +0.30–0.50 pts (30–50 bps) |
(Illustrative only; exact deltas depend on capital structure, PPA structure and lender methodology. The direction and magnitude are consistent with what we see in real transactions.)
The takeaway
Lower resource uncertainty translates directly into more debt capacity, better DSCR headroom and higher equity IRR. This is not because the project produces more energy, but because the confidence interval around that production is tighter. Every basis point of uncertainty you can defensibly remove earns real money.
The bottom line
Every project benefits from better data, from prospecting and distributed generation projects, through utility-scale finance and O&M. Below are key considerations for choosing the right dataset to meet your needs.
- NSRDB is a valuable free dataset, but its accuracy gap versus SolarAnywhere V4.1 is material at every timescale.
- SolarAnywhere V4.1 delivers ~75% lower annual bias on GHI, with tighter per-site consistency and flatter year-over-year residuals.
- The SolarAnywhere historical record stays accurate all the way back because we reprocess with each new model release, which is critical for defensible long-term P50/P90 estimates.
- SolarAnywhere runs in real time, with data available through the present and forecasts out to +14 days. NSRDB, by contrast, publishes on a lag. 2025 data landed roughly six months into 2026, and its availability depends on federal funding. If your long-term average leans on the most recent 5-7 years, a missing trailing year is a meaningful slice of the data that matters most.
- SolarAnywhere is priced to be accessible at every scale, including Typical-Year and Time-Series datasets, and high-resolution 1-minute and 500 meter options for the projects that need them.
As the world surpasses 3 terawatts of PV capacity this summer, Clean Power Research® continues the work of driving maximal accuracy out of SolarAnywhere solar resource data to help the industry finance their projects responsibly and keep costs low for its customers.
If you’re evaluating datasets for your next project or portfolio, we’d like to send over our most recent validation whitepapers or walk you through our benchmarking studies in more detail. Visit the links below to explore SolarAnywhere historical and real-time data options, or contact us to learn more.