Solar, meteorological, and environmental data

In this document

You will learn the importance of high-quality solar, meteorological, and environmental data in solar project assessment, the data retrieval challenges, best practices, and the Solargis approach to the subject.

Essential data for solar project assessment

Solar, meteorological, and environmental data provide the key information for evaluating site suitability, potential energy generation, and overall project viability:

Solar radiation data are vital for estimating potential energy production from photovoltaic systems.
Meteorological data include temperature, humidity, wind speed, precipitation, and snow, and they affect the performance efficiency and degradation of photovoltaic systems.
Environmental data include ground surface albedo, atmospheric parameters, terrain, and land use that impact reflected radiation and shading and determine project suitability.

Systematic accuracy and quality of the site data are paramount. Even minor discrepancies can lead to significant variations in project feasibility and financial viability. High-quality data ensues optimal design and operational efficiency while reducing weather and location-specific risks.

Data challenges

The vast amounts of satellite, meteorological, atmospheric, and environmental data from measurements and models require sophisticated retrieval, processing techniques, and numerical models to provide standardized time series data for any location on the Earth. The development and operation of models are essential for providing multiple parameters needed in calculating solar energy potential.

Requirements for data inputs to solar projects

Comprehensive: Utilizing data from models, satellites, and measurements in Geographic Information Systems (GIS) to provide standardized solutions globally.
Accurate: Leveraging model data and ground-based measurements to ensure reliable estimates for any geographic location.
Robust data management: Implementing cloud-based platforms for efficient data processing and sharing among stakeholders.
Standardized formats: Using industry-standard data formats and protocols to integrate data from various sources seamlessly.
Systematic validation: Continuous update and validation procedures to maintain high accuracy and reliability.
Advanced analytics: Sophisticated analytics is employed to systematically monitor data quality.
Compliance: Adhering to standards and best practices, including security and data privacy.

The Solargis approach

At Solargis, we are dedicated to providing high-quality data for solar energy projects worldwide. As a leading data provider, we collect and process extensive datasets from various sources and apply rigorous assurance procedures to deliver reliable inputs for solar energy evaluation.

Our input and output data

The input data

We employ data from satellite-based observations, meteorological and environmental models, and ground-based measurements to acquire inputs for our models, algorithms, and data processing infrastructure.

Satellite observations

Meteorological satellites, such as Meteosat, GOES, MTSAT, and Himawari, play a crucial role in cloud modeling. Data from national and international organizations, such as EUMETSAT, National Oceanic and Atmospheric Administration (NOAA), and Japan Meteorological Agency (JMA), operate satellites that provide key inputs for calculating historical, recent, and forecasting cloud cover data for solar radiation modeling.

Meteorological reanalysis models and numerical weather prediction models

We utilize historical meteorological data from reanalysis models such as ERA5, ERA5-Land, CSFR, and MERRA-2 to prepare historical time series data. Atmospheric data, such as aerosols, we acquire from the Copernicus CAMS service. For solar power forecasts, we integrate data from global Numerical Weather Prediction (NWP) models such as IFS, GFS, and ICON, along with regional NWPs like ICON-EU and HRRR. We acquire these data from systems operated by international and national agencies ECMWF, NOAA, NASA, and DWD.

Ground-based measurements

We use solar and meteorological measurements from public meteorological networks and private projects to validate and calibrate our models and processing algorithms.

High-resolution environmental data

We process high-resolution global data, such as elevation, ground surface albedo, and land cover, vital for evaluating solar projects. For instance, the Shuttle Radar Topography Mission (SRTM) by NASA provides detailed elevation data that can be used to analyze shading effects on solar installations. We also use digital elevation models (DEMs) to account for terrain effects on solar irradiance and to process meteorological and atmospheric parameters.

The output data

We process the above-mentioned input data using our proprietary algorithms and models to generate a range of data products.

Output data

Historical Site-Specific Solar Radiation and Meteorological Time Series and Typical Meteorological Year (TMY) Data: We provide a long history (up to 31 years) of high-resolution time series data for any location on the land (with the exception of polar regions) that includes all solar and meteorological parameters that are important for detailed and reliable evaluation of power performance and energy yield of photovoltaic projects. For larger PV projects (size higher than 10 MWp) we propose a site adaptation of the models with local solar and meteorological measurements to deliver long-term estimates with lower uncertainty.
Operational Site-Specific Solar Radiation and Meteorological Time Series Data: This data is used for operational monitoring and regular performance evaluation of photovoltaic projects in operation. Very often, the data from models are combined with ground measurements to achieve higher resolution and higher accuracy.
Solar Power Forecasts: Our forecast services are used by power plant operators, traders, and energy management companies to manage energy supply and demand effectively.
Solar Resource Maps: Our maps provide visual representations of solar potential and weather events across regions, aiding in site selection for new projects and understanding of recent weather and forecasts.

How we process the data

At Solargis, we develop advanced solar radiation models that leverage satellite observations and atmospheric data to provide accurate solar resource assessments and enhance energy yield predictions for solar projects.

Models & techniques	What they do
Solar models	We employ in-house developed solar models to simulate the interaction between solar radiation and the Earth's atmosphere and surface to calculate and estimate solar radiation input for PV modules at any location on the Earth (with the exception of polar regions).
Atmospheric models	We use models that integrate atmospheric data to calculate important inputs for solar models, such as aerosols and precipitable water vapor.
Meteorological models	We develop and operate models that improve geographical and time representation of data from solar and meteorological models and measurements. We also compute special meteorological data needed by PV simulation models, such as PV module temperature.
Environmental models	We develop models that account for shading from surrounding terrain and objects, ground surface albedo, electrical losses due to snow cover and soiling of PV modules, and many others.
Machine learning	We use machine learning algorithms to analyze patterns in the historical data that enhance the accuracy of our solar models and solar power forecasts.
Statistical methods	We employ statistical methods to enhance our data analysis. Time series analysis helps us identify historical solar data trends and inform our forecasting models.
Spatial interpolation	In specific cases, we use spatial interpolation methods, such as splines, to estimate geographical distribution of parameters for which we have coarse model data or ad-hoc measurements.
Data integration	By integrating data from multiple sources, we create comprehensive and robust computing solutions for solar radiation and solar forecasts.
Security and backup	We operate secure, redundant, and resilient computing and data storage infrastructure that assures high availability of data flows in real-time.
Quality control algorithms	Our quality control algorithms incorporate automated validation and manual reviews of model data and observations to maintain data integrity over time and geographical regions. These procedures help identify and correct anomalies in the data inputs to our models datasets, ensuring the availability and reliability that our clients expect from Solargis.

Validation and data integrity

Validation of solar resource data is essential for the technical and financial feasibility of solar energy projects. It supports energy yield estimates and performance, and it includes the estimates of data uncertainty, which are sensitive to weather, geographic, and technology-specific factors.

Importance of Validation

Validation reduces uncertainty in the estimates of solar radiation, meteorological and environmental parameters. Without rigorous validation, projects may face conservative estimates that could discourage investments in solar projects, especially in geographically complex regions.

Benefits of Validation

Financial Planning: Rigorous validation leads to reliable long-term energy yield projections with reduced uncertainty. This is crucial for financial assessments and securing investments.
Technical Design: High resolution, site-specific, and low uncertainty time series data help optimize the design and performance of solar power plants while mitigating weather-related risks of PV underperformance or damage.
Improved Accuracy: Validation can reduce data uncertainty—for GHI typically 0.5% to 2% in low- to medium-uncertainty areas and 1.5% to 3.5% in high-uncertainty regions. Validation is applied in all key parameters.

At Solargis, we support projects by rigorous validation utilizing public solar and meteorological measurements that we systematically conduct as part of internal quality procedures. To project developers and owners, we also offer site-specific model adaptation services based on the use of local measurements.

For project-specific needs, our process includes comparing the model outputs with ground measurements, enhancing the representation of the model for the local geography, and reducing uncertainty. Our commitment to thorough validation practices positions us as a leader in delivering reliable solar resource and meteorological data essential for successful project development.

Data structure and accessibility

The Solargis databases are a vital component of our data collection and processing framework, providing comprehensive and continuously updated data for solar energy professionals. It integrates all our data sources to offer a rich repository of solar, meteorological, and environmental parameters.

With up to 31 years of historical data available, the Solargis database enables users to conduct thorough analyses and make informed decisions. This extensive archive features high-resolution data with a spatial resolution of up to 90 meters and a temporal resolution of up to 1 minute, ensuring accurate solar assessments across diverse geographies.

Access to the Solargis database is facilitated through delivery channels of the Solargis platform: (1) interactive apps, (2) API, and (3) push delivery (SFTP), allowing users to integrate solar and meteorological data into their applications seamlessly. This flexibility supports (pre)feasibility analyses, due diligence in project development, regular performance evaluation, and solar power forecasting. All delivery methods provide access to application-specific time series and typical meteorological year (TMY) data. Additionally, Solargis delivers map data by regular or ad hoc delivery as well as through standard web-based map services. Solargis data services can be easily accessed via third-party software.

Solargis data vs. competitors

Solargis offers bankable data products and services for more than 15 years. With our focus on scientific excellence, constant research and development, systematic data validation, in-built extensive quality control, and qualified customer support, we position our services as the most reliable and bankable worldwide.

Here is a comparison of the new Solargis Evaluate solution that includes the new Evaluate application with embedded Solargis data:

Feature	Solargis Evaluate	Other software solutions
Data products used	Solargis time series is used by default; we also support TMY for fast assessment and specific tasks	In the absolute majority of cases, TMY data is used; some software solutions allow for limited use of time series.
Historical representation	Data represent a history of up to 31 years, depending on geographical region.	A shorter history of data is offered in other data products.
Data time step	15-minute is standard; Time series and TMY can be prepared for up to 1-minute time resolution	The hourly time step is the industry standard; some software solutions allow for limited use of data with a sub-hourly time resolution.
Spatial resolution	Terrain shading in solar parameters is computed at the resolution of 90 meters, air temperature and relative humidity are downscaled to 1 km, and ground surface albedo is available at 0.5 and 1 km resolution; other meteorological parameters typically represent a grid cell of 12 and 25 km.	Solar and meteorological data parameters are usually offered in a lower spatial resolution.
How data is computed	We compute our data always for exact geographical reference of the project given by accurate latitude/longitude coordinates. We always apply the exact elevation of the project reference point with an accurate representation of the terrain horizon. This is why delivery of all parameters for a full historical time series at a 15-minute resolution takes Solargis Evaluate up to 90 seconds.	Data is often delivered for a predefined low-resolution grid, sometimes combined with spatial interpolation techniques.