We model & validate
Your go-to digital solution for unlimited access to high-resolution resource data
- Satellite imagery
- Parallax correction
- TMY Pxx
The cloud, radiation and precipitation properties are retrieved from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board of four different geostationary weather satellites:
- Meteosat-11 (PRIME), covering Europe and Africa with a 3x3 km² resolution. Data is available as from 2004.
- Meteosat-8 (IODC), covering the Europe, Africa, South-West Asia and the Indian Ocean with a 3x3 km² resolution. Data is available as from 2019.
- GOES-East' Northern Hemisphere Extension, covering North- and Central-America with a 2x2 km² resolution. Data is available as from 2019
- Himawari-8 Real-Time, covering Oceania and South-East Asia with a 2x2 km² resolution. Data is available as from 2021.
Identifying clouds, radiation & precipitation using CPP
The Cloud Physical Properties (CPP) algorithm is developed at KNMI to derive meteorological products from the Meteosat Second Generation (MSG) satellite. Based on the MSG-CPP, we identify the cloud, radiation & precipitation properties in 4 distinct steps:
First, cloud-free pixels are separated from cloud-contaminated & cloud-filled pixels with the GEO v2018 algorithms developed by the NWC SAF.
Second, the cloud optical and microphysical properties (i.e. top temperature, phase, optical thickness & particle size) are derived using algorithms developed in the CM SAF (Benas et al. 2017; Roebeling et al. 2006). The retrieval of most these properties relies on observations of solar backscattered radiation, and is thus limited to daytime (solar zenith angle smaller than 84°).
Third, the total surface solar irradiance and its direct & diffuse components are derived using the methods described in Greuell et al. 2013.
At last, the precipitation intensity is estimated based on the retrieved cloud properties during daytime (Roebeling & Holleman 2009) and based on statistical relationships with the observed infrared brightness temperatures (Brasjen et al. 2015).
A parallax correction corrects the derived irradiance properties for the relative position of both the satellite and the sun.
P50 or Pxx typical meteorological years (TMY) are created based on Cebecauer & Suri 2015, but different weighing actors are applied to tailor the creation of TMY's to the application of solar power simulations.
2022 external validation by Eurac: solar irradiation data
The high accuracy of our data has been confirmed by the Institute for Renewable Energy of Eurac research center. In its validation report, Eurac compared 3E's solar data to more than 420 years of ground measured data from the best public meteo stations in Europe. They concluded their paper by "the 3E solar dataset achived an overall normalized mean bias error of 0.48% with a standard deviation of 2.3%. The normalized root mean square error ranges from 18% for hourly data to 2.16% for annual data". Those excellent results make 3E solar irradiation data one of the most accurate datasets in Europe.
Download the validation paper of Eurac
2022 internal validation: satellite-based irradiation data
At 3E, we take data validation seriously. The mean bias error for our data is close to zero for the different validations, i.e. respectively 0.3% and 0.7% for the global or European stations. The absence of bias, combined with a low uncertainty of the bias, makes 3E’s satellite- based service a bankable source of irradiation data for long-term yield assessments (or LTYA) of photovoltaic systems. In this paper, we describe the permanent internal validation framework and the achieved results in detail.
Download Internal Validation Paper
2018 external validation: TÜV Rheinland
TÜV Rheinland was asked to perform a detailed 3rd-party validation of 3E’s satellite derived solar irradiation data. They have validated 3E’s satellite based solar irradiation data over 35 meteo stations in Germany. After processing, filtering and quality control of the data, the spatial aggregated results are: a mean bias of 0.7% with a standard deviation of 2.5% a monthly and daily RMSE of respectively 4.6% and 10.6%. TÜV Rheinland concluded that "this high accuracy from the results over all years confirms the excellent quality of 3E’s solar irradiation data (GHI) in the validated moderate-climate region."
Satellite-based irradiation data: stakeholder use cases for the solar PV sector
Our satellite-based irradiation service is a bankable data source for long-term yield assessments - and its low prediction error at high resolutions makes it a trustworthy source for performance monitoring. Developers, lenders and investors use our data to gain assurance when evaluating the feasibility, profitability, and risks of a project. If their assets under-perform, a strict financial penalty may apply which requires expensive risk mitigation measures. Owners, asset managers and O&M contractors use it to generate unambiguous KPI’s during the operational phase for an efficient & profitable management of their portfolios. In this paper, we describe a wide set of use cases for the solar PV sector specifically.