Meteonorm Comparison

Solcast is independently validated as the lowest uncertainty solar resource dataset
Historical and Tmy
Solcast's historical solar irradiance database was released in 2019, with a mission to provide highly accurate, validated data to the solar development community.

Using a proprietary cloud detection and identification model, pioneered by our CTO, Dr Nick Engerer, we’ve been able to produce the best performing solar database available. We've always thought developers should focus on planning and assessment, with fast and easy access to high quality, bankable data.

Meteonorm has been around for over 30 years and is used widely throughout the solar development community. The dataset is integrated into many PV simulation software. Meteonorm uses a fundamentally different way to model the solar resource, based on interpolated and synthetic data, instead of the satellite-derived, semi-empirical method used by Solcast.


Solcast Meteonorm
Free trial with instant access and data download?
Download wait time 1-9 minutes 1 minute
Mean Bias Error (GHI) 0.0% N/A
Bias Deviation (GHI) ± 2.0% N/A
90% of sites have bias (GHI) smaller than ± 2.5% N/A
Mean RMSE (GHI) 16.5% N/A
Comprehensive, global, independent validation
Validation Sites 46 N/A
Satellite based estimation
Global Coverage
Resolution of satellite data used 1-2 km 2-8 km
10+ years of satellite data at full temporal resolution
Ignores older, less reliable satellites

Source: Meteonorm Handbook part I: Software & Meteonorm Handbook part II: Theory

Model Inputs

Meteonorm has a fundamentally different approach to modeling solar irradiance than Solcast. Meteornorm relies heavily on data from weather stations; their key approach is the interpolation of long-term monthly-averaged values from nearby meteorological stations.

Data from well maintained and calibrated weather stations is usually a truer representation of the solar radiation and other weather parameters at a point site than that a satellite estimate, and perhaps a 5 to 10km radius around the station, depending on the topography and land surface. This is why many developers will place a pyranometer at a planned site, to verify the data they have used in the modeling process. The difficulty in constructing a database with global coverage using weather stations, aside from the data quality issues with the weather station measurements, is the uneven geographic distribution of stations, and the up-to 1,000km gap between them. Clouds and irradiance have poor spatial autocorrelation, which is why things can change markedly in as little as 5 to 10km away from the closest surface measurement site, sometimes even less.

Modeled data based on satellite imagery is generally incorporated only as support information in the Meteonorm dataset and used mainly when no meteorological station is available. This means the data must be interpolated between ground stations.

The most granular solar resource data used by Meteonorm from ~1300 weather stations is monthly averages, using these to statistically generate hourly values. This synthetic generation of a typical year dataset results in loss of the coherence between solar irradiance and air temperature. As the performance of solar power systems varies with solar irradiance and air temperature, use of synthetic hourly dataset increases the uncertainty of solar energy simulations.

Synthetic irradiance is an alternative option for obtaining data in a reasonably high temporal and spatial resolution. However, the result is not reflective of the actual irradiance at a certain point in time and can produce artifacts in the data. This effect can be seen in the homogenous regions of interannual variability in the image below. This approach has several limitations and can give misleading results, for obvious reasons.


When Meteonorm does use a satellite input to support ground measurements, a 2-3 km resolution scan every hour (Europe) or an 8 km scan once every 3 hours (rest of world) is processed. This large time step between data inputs of varied spatial resolution can provide misleading information on cloud formation, tracking, and opacity.

The Solcast methodology is semi-empirical and satellite-derived. We begin using validated, published models that offer excellent performance to build clear sky models. We combine this with the ‘secret sauce', our in-house methods for detecting and tracking clouds. This lets us model the amount and type of solar radiation reaching any particular 1-2 km2 grid cell. For more details see inputs and algorithms.

Accuracy & Validation

While Meteonorm provides uncertainty estimates along with data values for a particular site, there is little validation demonstrating database accuracy provided by Meteonorm. The lack of validation is not surprising; a one-on-one comparison with hourly ground measurements and the synthetic data Meteormon generates is not really possible. Some validation of satellite data is available, but statistics are not provided, so a comparison of the satellite-modeled solar resource with Solcast is not possible.

Without a publically available validation document or provision of accuracy statistics, developers are unable to understand possible errors in long-term estimates of Meteonorm solar data, or estimate the accuracy of energy simulations and operational calculations.

It is difficult to objectively compare Meteonorm with Solcast due to the different approaches to creating solar data. However, Solcast’s model has been independently validated using multiple locations globally. Solcast’s model has been compared with BSRN ground station measurements, available in the public domain.

Solcast Meteonorm
Mean Bias Error (GHI) 0.0% N/A
Bias Error Standard Deviation (GHI) ±2.0% N/A
90% of sites have bias (GHI) smaller than ± 2.5% N/A
Mean RMSE (GHI) 16.5% N/A

You can find more details on the Solcast dataset on the accuracy and validation page.


Meteonorm can appear to be cheaper from the first viewpoint. However, the proven bankability of Solcast's database offers better value by lowering risk for developers, investors, and operators of solar power plants. When decisions on development or operation of high-value solar assets are to be made, higher risk associated with the use of high-uncertainty data can be very expensive. View Solcast prices

Historic Data Products

Time Series
The complete suite of irradiance and weather data required for effective monitoring, operation, and forecasting at your large-scale solar farm.
Typical Meteorological Year (TMY)
The complete suite of irradiance and weather data required for effective monitoring, operation, and forecasting at your large-scale solar farm.