Validation of meteorological parameters

Overview

Factors such as air temperature, wind speed, and relative humidity play critical roles in PV simulation accuracy and they interact in complex ways. Together, they determine module operating temperature, changes on module’s spectral response, and the formation of soiling on the modules surface, together with other risks such as condensation and accelerated degradation.

Accurately incorporating these factors into PV simulations enables more precise estimation of operating conditions and energy output. To validate the accuracy of the meteorological data used in Solargis applications, we have compared sub-hourly values provided by the model with ground-measured data from high-quality publicly available stations around the world.

Ground reference data have been collected from high-accuracy instruments only. The data undergo a complete quality assessment before the comparison, to ensure that the uncertainty of the measured data is within the uncertainty of the instruments.

TEMP validation statistics

Accurate air temperature inputs are vital for calculating module temperatures, as PV efficiency decreases with rising operating temperatures due to the temperature coefficient of power.

Additionally, the lowest operating temperature affects system voltage and properly accounting for this ensures optimized string sizing, inverter compatibility, and safe operation in extreme environments.

The table below shows the summary of the accuracy statistics of Solargis air temperature data at 2 meters (TEMP) compared to high-quality ground measurements at more than 11,000 sites across all types of climates:

A mean bias of -0.1°C (24 h) and standard deviation of 1°C indicate excellent agreement with ground measurements, ensuring reliable energy yield predictions.

Night-time deviations are slightly higher, but daytime values—relevant for solar power generation—are estimated with higher accuracy.

The map below shows the sites at which the NWP-based data was validated against the ground-measured data and a representation of calculated bias.

WS validation statistics

Wind reduces module temperatures by dissipating heat, improving efficiency. High wind speeds enhance this cooling, while low wind in hot climates can lead to overheating.

Wind speed also impacts structural loads on PV systems, making it a critical consideration for stable and durable system design, even though it does not directly affect energy yield.

The table below shows the summary of the accuracy statistics of Solargis wind speed data at 10 meters (WS) compared to high-quality ground measurements at more than 11,000 sites across all types of climates:

A mean bias of 0.1 m/s (24 h) and standard deviation of 1.1 m/s demonstrate good accuracy.

Day-time and night-time deviations show slight variations, with night-time bias being higher, yet daytime data are highly reliable for energy simulations.

The map below shows the sites at which the NWP-based data was validated against the ground-measured data and a representation of calculated bias.

RH validation statistics

High water vapor content alters the solar spectrum by absorbing and scattering radiation, impacting PV module response. Spectral corrections in simulations account for this using location-specific atmospheric data.

Prolonged exposure to humid conditions can also accelerate PV component degradation, especially in tropical or coastal areas, highlighting the need for robust design and materials.

The table below shows the summary of the accuracy statistics of Solargis relative humidity (RH) compared to high-quality ground measurements at more than 11,000 sites across all types of climates:

The mean bias of 0% (24 h) and standard deviation of 7% reflect solid agreement with ground measurements.

The map below shows the sites at which the NWP-based data was validated against the ground-measured data and a representation of calculated bias.

Conclusions

• Solargis meteorological data is robust and suitable for optimizing PV projects globally, even in challenging environments. While meteorological models represent larger areas and cannot fully capture microclimates, they show high reliability for effective use in PV simulations.

• Validation conducted across 11,516 global sites covered diverse geographical and climatic conditions. Areas in more complex geographies with mountainous or coastal terrain, or abrupt microclimatic transitions, show higher deviations due to the inability of large-scale models to capture local conditions. Regions with sparse input data for numerical weather prediction (NWP) models also exhibit higher deviations.

• Deviations are generally higher at night, but accuracy during solar power generation hours remains consistently high, aligning with PV simulation needs.