Documentation

The Solargis model is validated at 220+ locations globally. The data are compared with quality-controlled GHI/DNI measurements available in public domain. A summary of the validation results for annual sums of GHI and DNI is given below:

See details on the Solargis accuracy overview page.

The Meteonorm database is based on combination of measured and modeled solar radiation data. For European locations, the Heliomont satellite-based solar radiation database is used. As per independent validation, the Heliomont model shows lower performance than Solargis. The CM-SAF database, used for locations in Africa, has been validated at only a couple of locations in Africa. For Americas, Asia, and Australia, no validation document has been published by Meteotest, and therefore accuracy in these regions cannot be objectively evaluated.

Although solar radiation data from 1,300 meteorological stations are incorporated, these stations are geographically unevenly distributed. Majority of them stopped operation in the past, they were equipped with low accuracy sensors and they were poorly maintained. Thus, use of the measured data from majority of these stations may not help improving the accuracy of solar radiation database. On contrary, there is a risk that combining low accuracy measurements with satellite-derived data has a negative impact on the accuracy of final outputs.

The primary source of measured Global Horizontal Irradiation data is the GEBA Global Energy Balance Archive. Independent researchers have the following comments about the GEBA database:

• "The quality of global radiation measurements is not very good as more than 10% of the monthly means were identified as being afflicted with gross errors. The gross errors were found to be random at the global scale." Source: https://journals.ametsoc.org/doi/pdf/10.1175/1520-0477%281999%29080%3C0831%3ATGEBA%3E2.0.CO%3B2
• "From totally 269 European GEBA stations with monthly mean data available between 2000 and 2007, 205 cover at least five years, 172 of them with less than 30% data gaps and at least one complete annual cycle."

  "We recommend careful quality control and homogeneity testing of SSR time series in GEBA in order to study merely climatic (free of non-climatic factors) variability and changes in solar radiation."

  Source: https://aip.scitation.org/doi/pdf/10.1063/1.4804866

Solargis solar radiation data is computed for each 250 m x 250 m grid cell. We make use of the best available and high-resolution data inputs that are harmonised in geographical space and time. The Solargis model has also been fine-tuned for all types of landscapes and climate conditions.

In Meteonorm, the key approach is interpolation of long-term monthly-averaged values from nearby meteorological stations. Modelled data based on satellite imagery is incorporated as support information and used mainly when no meteorological station is available within a distance of 10/20/30 km (in Europe/Africa/Rest of World). This approach has several limitations and can give misleading results in regions where solar resource varies considerably at short distances, e.g. in islands, coastal zones and regions with variable terrain. It should also be noted that different data inputs represent different periods of time, and the database is very heterogeneous from the perspective of time representation.

Hourly values of solar radiation and air temperature in Meteonorm typical year (or TMY) files are not based on real observations but are statistically generated from monthly averages. The synthetic generation of typical year dataset is a mathematical process that results in loss of the coherence between solar irradiance and air temperature. Also the frequency distribution of the hourly values in the resulting artificial time series may not represent real conditions of the climate. As performance of solar power systems varies with solar irradiance and air temperature, use of synthetic hourly dataset increases uncertainty of solar energy simulations.

In comparison, Solargis TMY data is constructed from hourly or sub-hourly time series of solar radiation based on actual satellite observations and meteorological model outputs.

In order to account for years with extreme weather conditions, it is recommended to use 10+ years of continuous historical data when estimating long-term solar resource potential. The temporal coverage of solar resource data in the Solargis database varies from 11+ to 24+ years, depending on geographical location.

In case of Meteonorm, adequate temporal coverage, given by satellite-derived solar resource data, is seen only in Africa (1993-2012). For rest of the world, temporal coverage of satellite-derived data is just 7 years. Such short period of the satellite database does not comply with the standards for climate assessment.

Meteonorm solar resource data are based on processing of hourly or 3-hourly satellite observations. Hourly observations are used only in Europe and Africa. 3-hourly observations can provide misleading cloud coverage information in regions that see fast changing cloud formations, mainly in tropical and temperate climate.

In Solargis, satellite data is processed with frequency of 10,15, or 30 minutes (depending on the satellite platform). This helps us better capture cloud movements, resulting in higher accuracy of sub-hourly solar radiation values, and of the longterm averages derived from such time series.

Access to time series data for recent time period (last months or year) is important for several reasons:

• With recent data, it is possible to make one-to-one comparison with ground observations to better understand accuracy of data
• Accuracy of long-term estimate can be improved. This is only possible when systematic deviations between modeled data and high quality ground observations can be identified
• Regular updates of recent data can be used for performance monitoring of operations PV power plants

Solargis data is updated regularly and recent data can be made available globally with daily or monthly updates.

In Meteonorm v7.2 and earlier, hourly values are synthetic and they do not represent any particular period of time. Therefore, a like-to-like comparison to measurements representing a particular time period is not possible. From v7.3 onwards (released Nov 2018) access to recent time series is also available. As of Dec 2018, time series for Europe, Africa, and Near East are calculated using satellite based models. For other regions, time series data are calculated using ERA5 reanalaysis model that provide data at lower resolution and lower accuracy compared to satellite based models. As the calculation methodology of recent time series data is different from that used for typical year (TMY) files , the benefits of having access to recent time series data are limited.

In order to be fair to the Meteonorm team, we also want to list some features where they do better than us:

1. Data for 30+ weather parameters. Although our database does not include as many weather parameters, we do provide harmonised access to all parameters relevant for solar energy assessment
2. Data export in 36 formats. Although we do not offer such flexibility, our data can be easily imported to the most popular energy simulation software, such as PVsyst, SAM, and others

To be fair to ourselves, there are some more features that Solargis offers but Meteonorm does not:

1. High resolution multi-year time series data available globally
2. Validated PV simulation application with extensive functionalities
3. High resolution interactive solar resource maps
4. Possibility to download data on device with non-Windows operating system

Meteonorm can appear to be cheaper from the first viewpoint. However in long-term Solargis offers better value for money and much lower risk for developers, operators and investors into solar power plants.

Hundreds of organisations worldwide, big and small, have chosen Solargis, because they value making decisions on basis of reliable data. In particular, 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.

Please note that we make available annual average values for free. If you are looking for more detailed information, and want to reduce risk and save money in the long-term, spending a little more for accurate solar resource and meteorological data from Solargis is the right choice.

This article was originally published in Jan 2017 and has been last updated on 3 Dec 2018. In case Meteonorm gets an update, and some of the information mentioned above becomes outdated, please let us know and we will be happy to update this article.