Prospect map layers

  • Updated on May 23, 2025
  • Published on Feb 18, 2025
  • 32 minute(s) read
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In this document

We will introduce the map layers available in the Prospect application.

Prospect maps

The wide range of map layers the Prospect application offers serve as a powerful resource for evaluating the environmental, climatic, and geographic factors that influence photovoltaic (PV) system performance and reliability. By integrating advanced mapping methodologies and validated datasets, these maps provide actionable insights into solar resource potential, atmospheric conditions, and risk indicators across diverse global locations.

Each map layer contains a brief description, parameter definition, information about its Solargis mapping process, and why you should consider it. They are categorized into four categories for better navigation:

Map layers included:

  • Satellite map

  • Topographic map

  • Terrain elevation above sea level (ELE)

  • Terrain slope map (SLO)

  • Terrain azimuth map (AZI)

  • Land cover map (LANDC)

  • Population density map (POPUL)

  • Photovoltaic power production map (PVOUT csi)

Map layers included:

  • Global horizontal irradiation (GHI)

  • Direct normal irradiation (DNI)

  • Direct to global horizontal irradiation ratio (D2G)

  • Global irradiation for optimally tilted surface (GTI opta and OPTA)

  • Long-term solar resource variability (GHI VAR long)

  • Short-term solar resource variability (GHI VAR short)

  • Solar resource seasonality index (GHI VAR season, DNI VAR season)

Map layers included:

  • Air temperature at 2 m above the ground (TEMP)

  • Daily module temperature amplitude higher than 50 °C (TMOD AMP50)

  • Relative humidity (RH)

  • Wind speed at 10 m above the ground (WS)

  • Wind Gust p99 (WG p99)

  • Precipitation (PREC)

  • Snow days (SNOW)

  • Cooling degree days (CDD)

  • Heating degree days (HDD)

Map layers included:

  • Ground albedo (ALB)

  • Lowest expected operating temperature (TLEO)

  • Highest expected operating temperature (THEO)

  • Precipitable water (PWAT)

  • Ultraviolet Radiation A and B (UVA, UVB)

  • Corrosion degradation rate (CORR)

Note: You will find more details about most map definitions, mapping processes, and why to consider them in the sections below.

General maps

Terrain elevation above sea level (ELE)

Terrain characteristics strongly impact local ecosystems. Elevation is the most influential terrain parameter, directly shaping local climate and atmospheric conditions.

Terrain also affects human activities, influencing transport routes, energy use, accessibility, and land utilization. The environmental footprint of human activity varies greatly between flat and rugged or dissected terrain.

Definition: Terrain elevation above sea level is the height of a land point relative to average ocean level, measured in meters or feet.

Solargis uses terrain elevation data at 3 arcsec (≈90 m) resolution, sourced mainly from the Shuttle Radar Topography Mission (SRTM v4.1) for land between 60°N and 56°S. Polar regions use Viewfinder Panoramas3'' (Jonathan de Ferranti). Gaps are filled with ASTER GDEM V2, and bathymetry comes from GEBCO 30''.

Elevation values may differ from other models due to:

  • Different data sources and spatial resolutions.

  • Model imperfections (noise, interpolation errors, terrain representation limits).

  • Varying vertical reference systems (e.g., SRTM uses EGM96 geoid for mean sea level, others may differ), leading to discrepancies, especially in complex terrain or where geoid varies significantly.

  • Accurate elevation data for the site and surroundings is essential for solar plant design. Nearby elevated terrain can cause shading, affecting energy yield—Solargis Prospect evaluates this in the Site detail section.

  • Including man-made or natural obstructions (buildings, trees) in shading analysis is possible if their height and location are known. Shading significantly impacts PV module performance and must be carefully assessed.

  • Understanding terrain geometry also helps mitigate gravity-driven hazards like water runoff and debris accumulation, reducing operational risks.

  • High-altitude sites (above 2,000 m, especially 4,000 m) face increased UV radiation, stressing PV materials and potentially reducing durability and performance. Sites below sea level or near water bodies require careful flood risk evaluation.

Terrain slope and azimuth maps (SLO, AZI)

Definition:

  • Terrain slope (SLO) measures the steepness of land—how much elevation changes over a set horizontal distance. It is expressed in degrees (°), representing the angle between the land surface and a flat plane.

  • Terrain aspect or azimuth (AZI) indicates the compass direction a slope faces, measured in degrees clockwise from north (0° = North, 90° = East, 180° = South, 270° = West). Slopes up to 1° are considered flat; 1–2° are quasi-flat, where aspect is undefined due to minimal incline.

SLO and AZI maps are calculated from terrain elevation data (ELE) using raster algebra over a 3×3 pixel area, with input data at 3 arcsec (≈90 m) resolution.

Terrain geometry is vital for environmental analysis, construction, water flow, erosion, stability, and especially solar energy design. Slopes facing the Equator receive more solar radiation, making them optimal for solar harvesting.

For utility-scale photovoltaic (PV) systems on sloped or uneven ground, proper row spacing is needed to avoid self-shading, where one row shadows another and reduces energy yield.

In complex terrain, PV plant design and performance modeling become more challenging. High-quality terrain data is essential for accurate shading simulation and minimizing energy losses from suboptimal layouts or topography-induced shading. Such optimizations can be performed in Solargis Evaluate.

Land cover (LANDC)

Urbanization, agriculture, and infrastructure development have greatly altered natural land cover worldwide, affecting ecosystems and climate. Careful planning and sustainable management are needed to reduce environmental harm and ensure fair access to energy resources.

Definition: Land cover, as defined by the FAO, is the observed physical cover on the earth’s surface, including both vegetation (natural or planted) and human-made structures.

The LANDC map uses C3S global land cover maps (300 m resolution, v2.1.1), classifying land into 22 types based on the UN FAO Land Cover Classification System (LCCS). Land cover is identified using time-series satellite imagery from multiple missions (1992–present). Detailed methods are available on the Copernicus program websites.

Utility-scale solar power plants require significant land, and the current land cover must be considered, as it will change during development and operation. National and local regulations often restrict which land cover types can be used for energy projects. The LANDC map offers essential preliminary information for planning and decision-making.

Population density (POPUL)

Energy needs vary widely due to uneven global population distribution. Careful planning of energy generation and infrastructure is required to meet regional demands.

Definition: Population density is the number of people living in a given area, expressed as persons per square kilometer.

The POPUL map uses Gridded Population of the World (GPW) data, refined by Solargis, at 30 arcsecond resolution (≈1 km). Source data quality varies by country and census detail, but the map reliably shows population distribution for broad planning and analysis.

Population density affects energy planning:

  • High-density areas have greater energy demand but limited land and higher costs, making large-scale projects more challenging.

  • Low-density areas may lack a local workforce and essential infrastructure, creating logistical and financial hurdles for energy projects.

Photovoltaic power production (PVOUT csi)

The map provides an overview of the estimated potential for solar photovoltaic (PV) power generation. It represents the average yearly totals of electricity production from a 1 kW-peak grid-connected PV power plant, configured with the most common technical specifications. This standardized approach allows for consistent benchmarking of solar energy potential across different locations and regions worldwide.

Definition: The solar electricity output is calculated using high-resolution solar resource data and advanced PV modeling software developed by Solargis. The modeled PV system consists of ground-mounted, free-standing crystalline silicon (c-Si) PV modules fixed at an optimal tilt angle toward the Equator to maximize annual energy yield. High-efficiency inverters are assumed in the setup, with losses due to dirt and soiling estimated at 3.5% and cumulative conversion losses (e.g., shading, mismatch, inverters, cabling, transformers) assumed at 7.5%. The system is modeled with 100% availability, ensuring no downtime. For alternative configurations or site-specific setups, users can utilize the Solargis Prospect PV Configurator for tailored calculations.

To generate global PVOUT maps efficiently, Solargis employs a streamlined version of its PV calculator optimized for large-scale mapping. Calculations are performed at a spatial resolution of 30 arcseconds (~1 km), creating continuous gridded datasets with long-term yearly and monthly summary statistics. The model incorporates solar radiation, air temperature, and terrain data to simulate energy conversion processes and account for system losses. While this mapping approach ensures rapid and reliable results for global comparison, the full-featured Solargis Evaluate tool is available for more detailed site-specific analysis.

The PVOUT map provides a quick and reliable estimate of photovoltaic power generation for a standard system configuration. This information is invaluable for comparing solar potential across regions and identifying promising locations for further investigation. However, as actual PV configurations may vary significantly from the standard setup used in this map, users are encouraged to refine their analysis using location-specific inputs and custom configurations in dedicated tools like the Solargis PV Configurator. This ensures more accurate predictions tailored to specific project requirements.

Solar resource maps

Global Horizontal Irradiation (GHI)

This solar resource map provides an estimate of the solar energy available at the Earth's surface for power generation and other energy applications. It offers a foundational parameter for assessing solar potential and optimizing energy systems.

Definition: Global Horizontal Irradiation (GHI) is the total amount of solar radiation, including both direct and diffuse components, that reaches a horizontal plane on the Earth's surface. It represents the sum of direct sunlight and scattered light from the atmosphere. GHI closely approximates the data measured by a pyranometer at ground level. However, it is important to note that while pyranometer observations are specific to a single point, GHI values derived from models represent averages over a grid cell area (pixel), which can influence comparisons between measured and modeled data.

The GHI data is calculated using the Solargis solar model, which integrates atmospheric and satellite data at time intervals of 10, 15, or 30 minutes. These high-frequency time series are then aggregated into long-term yearly and monthly statistics to create gridded data layers. The mapping process accounts for terrain effects at a nominal spatial resolution of 250 meters, ensuring accurate representation of solar resource variability across diverse landscapes.

Understanding GHI is critical for estimating how much solar energy a specific location receives, making it an essential parameter for sizing and optimizing photovoltaic (PV) systems. As the primary input for energy yield calculations, GHI directly influences decisions on site selection, system design, and performance expectations. This map enables users to assess solar potential efficiently and supports informed decision-making in planning solar energy projects.

Direct Normal Irradiation (DNI)

The DNI solar resource map provides an estimate of the solar energy available for power generation and other applications, with a particular focus on systems utilizing solar tracking technologies. It is a critical parameter for assessing the performance of advanced solar systems such as concentrating solar power (CSP) and tilted or sun-tracking photovoltaic (PV) modules.

Definition: Direct Normal Irradiation (DNI) represents the portion of solar irradiance that directly reaches a surface oriented perpendicular to the Sun's rays. Unlike Global Horizontal Irradiation (GHI), which measures radiation on a horizontal surface, DNI focuses exclusively on direct sunlight without accounting for diffuse radiation. This parameter closely aligns with what a pyrheliometer would measure at ground level. However, it is important to note that while pyrheliometer readings are specific to a single location, modeled DNI values represent averages over a grid cell area (pixel), which can influence comparisons between observed and modeled data.

The DNI data is calculated using the Solargis solar model, which integrates atmospheric and satellite data at time intervals of 10, 15, or 30 minutes. These high-frequency time series are aggregated into long-term yearly and monthly statistics to create gridded data layers. The mapping process accounts for terrain effects at a nominal spatial resolution of 250 meters, ensuring accurate representation of solar resource variability across diverse landscapes.

Understanding DNI is essential for optimizing solar tracking systems and CSP technologies, where direct sunlight plays a dominant role in energy conversion. It is also critical for calculating global irradiation received by tilted or sun-tracking PV modules. As one of the most important parameters for energy yield calculations and performance assessments, DNI enables project developers to design systems that maximize efficiency and output. This map helps users identify regions with high direct sunlight potential, guiding decisions on site selection and system configuration for advanced solar applications.

Direct to global horizontal irradiation ratio (D2G)

Definition: The Direct-to-Global Horizontal Irradiation Ratio (D2G) measures the proportion of diffuse horizontal irradiation (DIF) within the total global horizontal irradiation (GHI), using long-term average data. A higher D2G value means that diffuse light makes up a larger share of the solar resource at a specific location. While D2G can theoretically range from 0 to 1, actual values at the Earth's surface typically fall between 0.15 and 0.75.

D2G is calculated using raster (array data) algebra as follows:

D2G = DIF / GHI

Both DIF and GHI are generated by the Solargis solar radiation model. In Prospect, the D2G map displays long-term average values, but D2G can also be calculated for different time periods, such as yearly, monthly, or other time-series formats. The highest D2G values are usually found in coastal regions with cold climates-such as the Faroe Islands or western Patagonia-where persistent cloud cover is common. High D2G values also occur in areas with significant atmospheric aerosols, like the Sichuan Basin in central China. In contrast, the lowest D2G values are found in arid regions such as western Australia, southwestern Africa, and the highlands along the Chile–Argentina border, where clear skies and low atmospheric moisture lead to more direct sunlight.

For most solar energy projects, locations with a higher proportion of direct sunlight are preferred, as modern PV technologies generally produce more energy from direct light than from diffuse light. However, some PV technologies-such as thin-film modules-perform better under diffuse conditions compared to others, like crystalline silicon modules.

When comparing locations, looking only at absolute DIF values can be misleading. For example, Cape Town (South Africa) and Munich (Germany) may have similar amounts of diffuse irradiation, but Cape Town’s total solar resource (GHI) is nearly double. The D2G ratio highlights this difference by showing the relative share of diffuse light.

Global irradiation for optimally tilted surface and optimum tilt (GTI opta and OPTA)

Definition: Global Tilted Irradiation (GTI) is the total amount of solar radiation-both direct and diffuse-that strikes a tilted surface, such as a photovoltaic (PV) module. Unlike a horizontal surface, a tilted surface also receives a small amount of ground-reflected radiation. GTI is a key factor in determining the performance of PV technology.

Optimum tilt (OPTA) is the fixed angle at which a surface (for example, a PV module) is tilted towards the Equator to maximize the yearly energy yield from GTI.

GTI is calculated using Global Horizontal Irradiance (GHI), Direct Normal Irradiance (DNI), terrain albedo, and sun position. Calculations are performed in 15-minute intervals using models for diffuse irradiance on tilted surfaces and angular reflection losses. The resulting time-series data is then aggregated into long-term yearly and monthly statistics and formatted as gridded data layers. Terrain effects are included at a nominal spatial resolution of 250 meters.

OPTA is determined by finding the tilt angle that maximizes GTI over a year, taking into account local weather patterns and ground reflectivity (albedo). This calculation is performed at a spatial resolution of 2 arcminutes (approximately 4 km), which means it does not consider microclimatic variations or shading from nearby terrain. It also does not include system-specific design factors such as inter-row shading or the impact of temperature on PV output.

While GHI measures the solar resource available on a horizontal surface, GTI is the critical parameter for PV power generation. The difference between GHI and GTI increases with distance from the Equator, both north and south.

Note 1: In this context, GTI is calculated for a fixed PV module set at the optimum tilt angle (OPTA), which is a reliable estimate for many PV projects. However, for detailed PV system design, GTI should be calculated for the actual mounting configuration, which may include tracking systems. If the system design is known, always use the corresponding GTI calculation.

Note 2: PV system design is often optimized for other factors, such as reducing inter-row shading, improving winter energy yield, or fitting rooftop geometries. Therefore, the actual tilt angle may differ from OPTA. Understanding the energy loss from using a suboptimal tilt provides valuable insights into system performance and potential efficiency improvements.

Long-term solar resource variability (GHI VAR long)

Long-term variability of Global horizontal irradiation (GHI) describes the cycles of solar resource year-by-year and over decades. Solargis Prospect now includes a global map and data detailing interannual solar resource variability.

Definition: GHI VAR long in Solargis Prospect is characterized using the standard deviation of the yearly time series of GHI, expressed as a relative value (percentage).

Firstly, Solargis Time Series data, specifically the GHI parameter, is aggregated into yearly time series for each point in a grid that covers the entire world. The standard deviation is then calculated from the aggregated yearly time series. This standard deviation is used as the metric of the long-term variability of solar resource.

Developers and investors need to take into account the uncertainty related to the long-term variability of solar resource. The yearly changes in solar resource cause variability in the power output of a PV power plant, which in turn affects financial planning.

A higher value indicates greater interannual variability, signifying a larger dispersion of yearly summaries in relation to the long-term average (e.g., values span a wider range). The map representation allows comparing the stability of solar resource between locations or regions.

Short-term solar resource variability (GHI VAR short)

Short-term (or intra-day) variability is connected to the actual state of the atmosphere and clouds, and reflects the solar resource intermittency caused by clouds during the day. The parameter mapped in this layer is Global horizontal irradiation.

Definition: GHI VAR short can be characterized by multiple metrics. Here, we selected the yearly average count of GHI ramps exceeding the 300 W/m2 threshold, analyzed from Solargis GHI Time Series.

GHI VAR short is based on analysis of the sub-hourly time series of GHI from the Solargis database. It is based on harmonized 10-minute or 15-minute time series grid data of GHI representing years 2019 to 2023 (era of modern meteorological satellites available globally), with a nominal spatial resolution of 4 km (specifically 2 arcmin). The harmonized time series is further processed in the global grid as follows:

  • Calculation of solar noon for each day of the year.

  • Sub-setting Solargis GHI Time Series to ±3 hours from the solar noon.

  • Calculation of differences in the consecutive time slots.

  • Counting the ramps and calculation of other statistics from the filtered dataset.

  • Merging of the data  into a single seamless global dataset and map composition.

More detailed information is available in our contribution at EU PVSEC 2024 in Vienna.

Sudden changes in solar resource availability are caused by forming and moving clouds. Specifically, moving scattered (intermittent) clouds tend to cause frequent changes in solar irradiance. Solar resource intermittency is a challenge for the operation of PV power plants and the balancing of grids.

GHI VAR short map is valuable as an indicator for sizing and management of PV systems and short-term energy storage or other compensation mechanisms, assessment of performance of PV power plants (e.g., indication for clipping losses), as a siting factor for new solar parks, and ultimately a key resource for grid management.

Solar resource seasonality index (GHI VAR season, DNI VAR season)

Definition: The seasonality index measures how much solar resource availability changes throughout the year. It is calculated as the ratio between the long-term average monthly total for GHI (Global Horizontal Irradiance) or DNI (Direct Normal Irradiance) in the month with the highest value and the month with the lowest value. This index shows how consistent or variable the solar resource is over a typical year.

In theory, the seasonality index ranges from 1 to infinity. A value of 1 means the solar resource is evenly distributed across all months, with no seasonal variation. In real-world conditions, values usually range from 1.1 to 20.0. In extreme cases-such as near the poles or in complex mountainous areas-seasonality can be higher than 20. For DNI, if a location receives no direct irradiation for at least one month, it is considered out of range.

The GHI and DNI seasonality indices are calculated using raster (array data) algebra as follows:

GHI seasonality = max(GHI monthly long-term averages) / min(GHI monthly long-term averages)

DNI seasonality = max(DNI monthly long-term averages) / min(DNI monthly long-term averages)

Long-term monthly averages for GHI and DNI are produced from Solargis solar radiation model outputs using aggregation statistics.

Solargis maps show that seasonality generally increases with distance from the Equator, becoming more pronounced toward the poles. However, regional anomalies exist, especially for DNI, due to specific atmospheric patterns, such as the monsoon in South Asia or the influence of Atlantic weather systems in Europe compared to East Asia.

Understanding seasonal variation is essential for energy planners, system designers, and policymakers. It informs decisions about energy mix, storage needs, and grid integration.

Low seasonality (index 1–2): Indicates a stable solar resource throughout the year, typical in equatorial and tropical regions.

Moderate seasonality (index 2–5): Shows noticeable variation across the year, common up to 45° latitude.

High seasonality (index >5): Reflects strong seasonal swings, with much higher solar availability in summer than in winter, typical for temperate and high-latitude regions.

For DNI, understanding seasonality is particularly important when selecting solar technologies. Areas with high DNI seasonality may be better suited for fixed-mount systems, while regions with more stable DNI year-round are ideal for tracking systems.

Note: High GHI seasonality does not always mean high seasonality in PV electricity output (PVOUT). This is because lower winter solar irradiance often coincides with lower ambient temperatures, which actually improve PV efficiency. As a result, the seasonal variation in actual PV production is often less than the variation in GHI alone.

Climate maps

Air temperature at 2 m above the ground (TEMP)

Air temperature is a familiar part of daily life and a key environmental factor. It defines ambient conditions around structures and is critical in materials engineering, directly influencing the durability and performance of solar energy systems.

Definition: Long-term average air temperature measured at 2 meters above ground.

The TEMP map is created from ERA5-Land and ERA5 climate reanalysis data (ECMWF and Copernicus Climate Services), with native resolutions of 0.1° (≈11 km) and 0.25° (≈28 km). Long-term monthly and annual averages are calculated from hourly data (1994 to present).

To improve detail, especially in complex terrain and coastal areas, a spatial disaggregation method is used. This applies lapse-rate corrections based on elevation and multi-level air temperature data, resulting in TEMP values at 30 arcsecond resolution (≈1 km).

Air temperature is the second most important factor (after solar irradiance) affecting PV system output. As air temperature rises, PV module temperature increases—often exceeding ambient levels due to solar heating. Most crystalline silicon (c-Si) PV modules lose about 0.4% to 0.5% output per °C above 25°C (standard test conditions). In hot climates, this can cause significant performance losses, especially without adequate cooling.

Note: Other technologies, like amorphous silicon (a-Si), have lower temperature coefficients (–0.1% to –0.25% per °C) and are less affected by heat, but their lower efficiency means c-Si remains the most widely used.

Daily module temperature amplitude higher than 50 °C (TMOD AMP50)

The day-night thermal cycling of PV modules causes temperature fluctuations that impact performance and lifespan. Although the occurrence of extreme temperature amplitudes may not be frequent, they can contribute to the accumulation of thermal stress over the operational lifespan of PV systems.

Definition: TMOD AMP50 is calculated as the average number of days in a year with daily PV module temperature amplitude higher than 50 °C.

Firstly, the time series of PV module temperature (TMOD) was calculated in a grid covering the whole world. The inputs for the calculation of PV module temperature are hourly time series of air temperature at 2 meters, and hourly time series of Global horizontal irradiation (GHI), both from the ECMWF ERA5 meteorological model.

A simplified NOCT (Normal operating cell temperature) thermal model was used with a relatively high NOCT value of 48°C and Global horizontal irradiation (GHI) instead of Global tilted irradiation (GTI) to calculate PV module temperature TMOD:

TMOD=Tair+GHI*(NOCT-20)/800

As the largest daily amplitude of module temperature occurs in the hot season (with high Sun elevation and high solar irradiance values), the use of GHI instead of GTI does not significantly impact the results. Although the TMOD model is simplistic, it is relatively accurate, has been validated across a substantial number of PV systems installed worldwide, and is widely employed within the PV industry.

Lastly, the daily amplitude of the TMOD time series was calculated for each day in the dataset. The amplitudes over 50 °C were counted and averaged to obtain an average yearly number.

Thermal cycling is one of the recognized degradation mechanisms for a range of PV components. PV modules specifically must be qualified to withstand a defined limit of thermal cycles according to IEC 61215-1. Knowing the expected number of temperature cycles with a large amplitude at the project site is key to understanding the expected degradation rate of PV modules. This understanding allows the project developers to apply appropriate mitigation strategies. The map in Solargis Prospect additionally allows for a comparison of different regions and enables the consideration of the risk when setting up a PV project.

Reliative humidity (RH)

Atmospheric water vapour absorbs and scatters sunlight, reducing air transparency. High water vapour levels increase condensation and cloud formation, which diffuses direct solar radiation and significantly reduces sunlight reaching the surface.

Definition: Relative humidity (RH) is the percentage of water vapour in air compared to the maximum possible at the same temperature.

The RH map is calculated from dew point and air temperature at 2 meters above ground, using ERA5 reanalysis data (ECMWF and Copernicus Climate Services) at 0.25° resolution (≈28 km). The formula is:

RH = (DewTemperature / AirTemperature) × 100

Hourly data from 1994 onward is aggregated into long-term yearly and monthly averages.

The RH map supports regional comparisons and highlights areas with unusually high or low humidity. Monthly data reveal seasonal RH extremes and indicate cloud formation potential—regions with high RH often have more persistent cloud cover.

High RH also impacts PV system materials, accelerating degradation like corrosion, especially in metals. Combined with frequent freeze–thaw cycles (temperatures fluctuating above and below 0 °C), high humidity increases mechanical stress, leading to fatigue, cracking, or structural weakening over time.

Wind speed at 10 m above the ground (WS)

Wind affects PV installations in multiple ways. Mild winds cool the system, improving performance during hot weather. However, extreme wind events—including gusts—can cause damage from debris or excessive structural loads.

Definition: Long-term average wind speed measured at 10 meters above ground.

The WS map is derived from U- and V-wind components using ERA5-Land and ERA5 reanalysis data (ECMWF and Copernicus Climate Services) at 0.25° resolution (≈28 km). Long-term monthly and annual averages are calculated from hourly data spanning 1994 to the most recent year.

Wind speed shapes the operating environment for solar plants. The annual WS map enables regional comparisons and highlights areas with unusually high or low wind. Monthly data help identify seasonal wind extremes.

Sites with extreme winds should undergo detailed risk assessment using higher-resolution, preferably hourly or sub-hourly, data—such as that in Solargis Evaluate.

WS maps also indicate regional wind energy potential, supporting early-stage analysis for combined solar and wind projects. For detailed feasibility, use higher-resolution data and on-site measurements.

Note: Wind observations can vary locally due to terrain, vegetation, or buildings, so care is needed when comparing map data to local measurements.

Wind gust p99 (WG p99)

Wind gusts (WG) impact PV module stability and tracking systems, and their extremes can lead to energy losses and structural risks. Understanding wind patterns is essential for optimizing PV system design, ensuring long-term reliability, and preventing damage.

Definition: WG p99 is the 99th percentile of wind gusts at 10 m height above ground calculated from the ERA5 hourly dataset from the period 2001 to 2020, averaged to provide a yearly number. This percentile corresponds to the top approx. 80 occurrences of wind gusts within a year.

Wind gust is the maximum of the 20-second running average wind speed recorded within an observation cycle (typically 1 hour). It is commonly measured at 10 meters above ground.

To produce the map and data of WG p99 for Solargis Prospect, we used the hourly WG time series from the ECMWF ERA5 meteorological model. From this data, the 99th percentile value was calculated for each year between 2001 and 2020. The yearly values were then averaged to provide a single number for each location in a global grid.

Wind generates mechanical loads on PV structures, affecting durability and performance. Extreme wind speeds can damage PV modules and may induce invisible cracks. Moreover, severe wind gusts can force PV trackers into protective stow positions, thus reducing energy production. Site-specific wind assessments help in selecting appropriate designs of modules, trackers, and supporting structures.

Precipitation (PREC)

Precipitation, alongside air temperature, is a primary factor in climate classification and is crucial for both ecosystems and PV plant efficiency. The water cycle directly affects PV performance.

Definition: Precipitation (PREC) is the total amount of condensed atmospheric water vapor—rain and snow—that falls to the Earth's surface.

The PREC map is based on ERA5-Land and ERA5 reanalysis data (ECMWF and Copernicus Climate Services), with spatial resolutions of 0.1° (≈11 km) and 0.25° (≈28 km). Long-term monthly and annual averages are calculated from hourly data spanning 1994 to the most recent year.

Precipitation naturally cleans PV modules, reducing soiling and maintaining energy yield. In areas with low rainfall and high soiling, more frequent artificial cleaning is required, increasing operational costs (OPEX).

The timing of precipitation is as important as the total amount. Monthly data help identify whether rainfall is evenly distributed or concentrated in certain periods. Evenly distributed rain may eliminate the need for artificial cleaning, while regions with dry spells—even if annual precipitation is high—benefit from scheduled cleaning to optimize PV performance.

Snow days (SNOWD)

Snow cover varies by climate—remaining stable in some regions or changing frequently, especially where freeze–thaw cycles are common.

Definition: SNOWD indicates the average number of days per year (or month) with snow cover on the ground, where snow depth water equivalent (SDWE) exceeds 5 mm. Areas with SDWE below 5 mm are classified as having "occasional" snow.

The SNOWD map aggregates SDWE data from ERA5-Land and ERA5 reanalysis (ECMWF and Copernicus Climate Services), with resolutions of 0.1° (≈11 km) and 0.25° (≈28 km), plus Solargis post-processing. Long-term monthly and annual averages are computed from hourly data (1994 to present).

Snow impacts PV plants in two main ways:

Energy yield: Accumulated snow on PV modules blocks sunlight and stops generation. While ground snow doesn't directly measure module coverage, SNOWD is a useful first estimate for potential losses. Detailed analysis is available in Solargis Evaluate.

Structural load: Heavy snowfall adds mechanical stress to PV modules and mounting structures, requiring reinforced designs in high-snow regions.

Snow can also benefit PV systems by cleaning modules as it melts and slides off. Additionally, SNOWD maps can help identify areas with reliable snow cover—useful for winter sports planning

Cooling and heating degree days (CDD, HDD)

Cooling Degree Days (CDD) and Heating Degree Days (HDD) are metrics used in construction and energy sectors to estimate building energy needs for cooling and heating. These indicators reflect local climate, making climate type a key factor in building energy demand.

Definition:

  • CDD measures how much (in degrees) and how long (in days) outdoor air temperature exceeds a base temperature of 18°C, indicating cooling energy demand.

  • HDD measures how much (in degrees) and how long (in days) outdoor air temperature falls below the same base temperature of 18°C, indicating heating energy demand.

Global CDD and HDD maps are derived from hourly air temperature data (1994 to present) from ERA5 climate reanalysis (ECMWF and Copernicus Climate Services) at 0.25° resolution (≈28 km). Daily CDD and HDD values are first calculated, then aggregated into long-term monthly and annual averages.

Understanding seasonal building energy consumption is vital for architectural and HVAC design, as well as energy planning.

Regions with high CDD benefit from building-integrated photovoltaic (BIPV) systems, as peak cooling demand aligns with high solar irradiance during summer daylight hours.

In contrast, regions with high HDD may find BIPV systems less effective for heating needs, since heating demand peaks in winter when solar irradiance and PV output are low.

Environment maps

Ground albedo (ALB)

Reflection processes—measured as albedo—are a key part of the Earth's radiation budget and energy balance. The amount of solar energy reflected by the surface affects how much energy is available for solar power generation.

Definition: Ground albedo is the fraction of solar irradiance reflected by the Earth's surface, calculated as:

Albedo = Reflected horizontal irradiance / Global horizontal irradiance

It is dimensionless, ranging from 0 (perfect absorber) to 1 (perfect reflector). Most natural land surfaces have albedo values between 0.15–0.35; water bodies are lower (~0.05), while fresh snow or clean ice can exceed 0.85.

The ALB map is based on time-series MODIS satellite data (NASA) from 2006–2015, aggregated into long-term yearly and monthly averages. Gaps are filled with ERA5 reanalysis data (ECMWF and Copernicus Climate Services), and polar regions use additional sea ice data (NSIDC). The final resolution is 30 arcseconds (≈1 km).

With bifacial PV technologies, ground albedo is crucial for maximizing energy yield, as reflected irradiance boosts power generation. Albedo is a key input for PV performance simulations, influences global horizontal irradiance (GHI), and helps determine optimal module tilt angles and tracking strategies.

Highest expected operating temperature (THEO)

The highest expected operating temperature helps to understand the upper limits of temperature tolerance in PV systems. This understanding allows a comprehensive assessment of potential dangers and the implementation of appropriate mitigation measures.

Definition: The highest expected operating temperature is calculated using a methodology similar to that for the lowest expected operating temperature (TLEO), defined in the IEC 62738 standard. It is calculated as the average of the highest recorded air temperatures over 20 years at the site.

To calculate THEO, Solargis uses the ERA5-Land climate reanalysis data (from ECMWF and Copernicus services), with a grid resolution of 0.1° (~11 km) disaggregated to a 1 km grid. The highest annual air temperature at 2 meters height is calculated for each of the years between 2001 and 2020, and then averaged to obtain the final value of THEO. The air temperature data is validated against measurements from over 5,900 weather stations worldwide. The validation is outlined in our blog post about TLEO.

Extreme heat negatively affects the performance of PV systems, leading to reduced efficiency, shortened lifespans, and more frequent occurrence of failures, which may potentially be catastrophic. Several key components in PV systems are impacted:

  1. High temperatures can cause accelerated degradation in PV modules, reducing power output and potentially necessitating early replacement. While thermal runaway is less likely than with batteries, prolonged heat can cause performance issues.

  2. Inverters are vulnerable to overheating if their cooling is insufficient. If not properly managed, overheating can lead to failures that disrupt the entire PV system.

  3. Batteries, especially lithium-ion types, can experience thermal runaway in extreme heat conditions, leading to fires or explosions.

Precipitable water (PWAT)

PWAT measures the total moisture in a column of air, indicating how much water would fall as precipitation if all vapor condensed. It is different from actual precipitation (PREC), which is the water that reaches the ground.

Definition: Precipitable water is the depth of water (in mm or kg/m²) in a vertical column of the atmosphere if all water vapor were condensed and precipitated.

The PWAT map is calculated from total atmospheric water vapor, from ground to the top of the atmosphere, using ERA5 reanalysis data (ECMWF and Copernicus Climate Services) at 0.25° resolution (≈28 km). Hourly data from 1994 onward are aggregated into long-term yearly and monthly averages.

PWAT is valuable for understanding atmospheric moisture and its impact on solar radiation. While pyranometers and solar databases like Solargis provide broadband radiation data, they rarely offer spectral distribution. Models that account for PWAT improve the accuracy of spectral shift estimates and are increasingly used in PV energy modeling software.

PWAT is especially useful for time-series analysis, but long-term average maps give a first indication of typical PWAT levels and seasonal patterns.

Ultraviolet A and B (UVA and UVB)

Higher UV radiation from the Sun can accelerate the degradation of PV modules and other PV components, reducing their efficiency. It damages materials, creating free radicals that cause cracking and discoloration. Over time, it can also weaken encapsulation materials, leading to delamination and moisture damage.

Definition: The UVA and UVB maps and data in Solargis Prospect represent the average yearly summary of radiation within the respective spectrum.

The worldwide maps of UVA and UVB radiation represent the average annual UVA and UVB radiation calculated over the period ranging from 1994 to 2022. These parameters, though related to solar radiation, are derived from the global meteorological model ERA5 by ECMWF, using Solargis' spectral splitting methodology.

Radiation with a spectral width of 315-400 nm is considered UVA, and a spectral width of 280-315 nm as UVB. The inputs to the model for UVA and UVB radiation are:

  • Broadband UV radiation data available from ECMWF ERA5 model,

  • Total ozone column from ECMWF ERA5 model,

  • Aerosol optical depth AOD from ECMWF MACC-II

PV modules, non-shielded electrical cables, interconnectors, combiner boxes, and other exposed components of PV systems are constantly affected by UV radiation and undergo aging and degradation processes. UV radiation causes a photochemical effect within the polymer structure, which leads to degradation of the material.

The UVA and UVB maps and data provided in Solargis Prospect can be used to compare UV radiation between different geographical locations, assess the risk of premature failure or performance reduction, and adopt appropriate mitigation strategies, e.g., choice of components with higher UV resistance.

Corrosion degradation rate (CORR)

Corrosion is a degradation effect in PV modules, primarily driven by temperature and humidity. It weakens the electrical connections, increases leakage currents, and reduces power output over time.

Definition: Corrosion degradation rate (e.g., the rate of power output loss at the maximum power point of the PV module) is calculated using the Peck model with inputs from the ECMWF ERA5 meteorological model.

One of the most common models for corrosion is the Peck model, where the degradation rate DR is expressed as:

DR=A*(RHeff)n·exp(-E/(kB*TMOD))

where:

  • A - pre-exponential constant

  • RHeff - effective relative humidity inside the module in [%],

  • n – relative humidity impact parameter

  • E - activation energy of the degradation process in [eV]

  • kB - Boltzmann constant (8.62x10-5 [eV/K])

  • TMOD - module temperature in [K]

This model is used for the calculation of corrosion maps and data in Solargis Prospect. The atmospheric parameters have been obtained from ERA5 reanalysis. The values of A, n, and E provided by Kaaya et al. for the outdoor modules have been used, and the RHeff has been calculated with the model proposed by Koehl at al.

High temperatures and humidity accelerate corrosion, impacting PV module performance and longevity. Corrosion can occur alone or alongside other degradation modes like hotspots, soiling, or glass breakage. It is more prevalent in tropical and coastal regions but tends to worsen over time in any environment. Understanding corrosion risk helps with module selection, maintenance planning, and PV project siting.