Air mass directly impacts the performance of a pv module by altering the intensity and spectral distribution of sunlight that reaches it. Essentially, it’s a measure of the distance sunlight travels through the Earth’s atmosphere. The standard, AM1.5, is used for laboratory testing and represents the sun at a 48.2-degree angle. As the sun moves lower in the sky, the air mass value increases (e.g., AM2, AM3), meaning sunlight has to pass through more atmosphere. This longer path results in more scattering and absorption of light, reducing the power output of the module. The effect isn’t uniform either; different wavelengths of light are affected differently, which changes the very “color” of the light the solar cells receive.
The Physics of Light Attenuation and Spectral Shift
When sunlight enters the Earth’s atmosphere, it encounters molecules, aerosols, and water vapor. These interactions cause two primary phenomena: attenuation (weakening of the light) and spectral shift (change in the light’s composition).
Attenuation is straightforward: the longer the path, the more light is scattered (by air molecules) or absorbed (primarily by ozone, water vapor, and dust). This directly reduces the total irradiance, measured in Watts per square meter (W/m²), that strikes the pv module. For example, under a clear sky, the irradiance can drop from the standard 1000 W/m² at AM1.5 to roughly 800 W/m² at AM2 and even lower as the air mass increases further. This is a direct loss of the “fuel” for the solar cells.
Spectral Shift is more nuanced but equally important. The atmosphere does not treat all light equally. Shorter wavelengths, like blue and ultraviolet light, are scattered more easily (which is why the sky is blue). Longer wavelengths, such as red and infrared, are more readily absorbed by water vapor and carbon dioxide. As air mass increases, the spectrum that reaches the ground becomes increasingly biased toward the middle wavelengths. This is critical because different solar cell technologies have unique spectral responses; they are most efficient at converting specific wavelengths of light into electricity.
Impact on Different PV Technologies
The spectral shift caused by changing air mass affects various solar cell materials differently. This is quantified by a parameter called the spectral mismatch factor.
Crystalline silicon (c-Si), the most common technology, has a broad spectral response but peaks in the near-infrared region. Under higher air mass conditions, the reduction in blue light has less impact, but the strengthening of the red/infrared portion can be somewhat beneficial. However, the overall effect of reduced irradiance dominates, leading to a net decrease in power output.
Thin-film technologies like Cadmium Telluride (CdTe) and Copper Indium Gallium Selenide (CIGS) have different spectral responses. CdTe, for instance, has a response that closely matches the solar spectrum at AM1.5. When the spectrum shifts under higher air mass, its performance can be affected more significantly than silicon. The following table illustrates typical performance changes relative to AM1.5 conditions.
| Air Mass (AM) | Approx. Sun Angle | Relative Irradiance | c-Si Module Power Output (% of STC) | CdTe Module Power Output (% of STC) | Key Spectral Characteristic |
|---|---|---|---|---|---|
| AM1.0 | 90° (Overhead) | ~1050 W/m² | ~102% | ~101% | Enhanced UV/Blue, minimal atmosphere |
| AM1.5 | 48.2° | 1000 W/m² (Standard) | 100% | 100% | Reference spectrum for testing |
| AM2.0 | 30° | ~800 W/m² | ~78% | ~75% | Reduced Blue, slightly enhanced Red |
| AM3.0 | 19.5° | ~650 W/m² | ~62% | ~58% | Significantly reduced Blue, stronger Red/IR emphasis |
| AM5.0 | 11.5° | ~450 W/m² | ~40% | ~35% | Very weak overall, heavily filtered spectrum |
This data shows that while all technologies suffer from lower irradiance, the spectral differences lead to a slightly steeper performance decline for CdTe compared to c-Si at higher air masses. This is a crucial consideration for system designers in geographic locations where the sun is consistently low in the sky.
Quantifying the Performance Loss: The Air Mass Model
Engineers use empirical models to predict how a pv module‘s output will drop with increasing air mass. A common simplified model for relative power output (P_AM) compared to standard test conditions (P_STC) is:
P_AM / P_STC ≈ 1 / (AM)^x
Here, ‘x’ is an empirical coefficient that depends on the specific module technology and local atmospheric conditions. For a typical crystalline silicon module, ‘x’ is often around 0.75 to 0.85. Let’s calculate the expected power at AM2.0 using x=0.8:
P_AM / P_STC ≈ 1 / (2.0)^0.8 ≈ 1 / 1.74 ≈ 0.575 or 57.5%
This model predicts a more severe drop than the table above because it is a general approximation. Real-world conditions, especially atmospheric clarity, play a huge role. A hazy day at AM2.0 will have much lower output than a crystal-clear day at the same air mass.
The Interplay with Other Environmental Factors
Air mass never acts alone. Its effect is intertwined with other critical environmental variables that influence a pv module‘s performance.
Temperature: Higher air mass is often correlated with lower module temperatures because it occurs in the early morning and late afternoon when ambient temperatures are cooler. Since solar cells become less efficient as they heat up, this cooler temperature can partially offset the power loss from reduced irradiance. A module operating at 25°C (STC temperature) will produce more power than the same module operating at 45°C, even if the irradiance is identical.
Atmospheric Conditions: The composition of the atmosphere is a massive variable. A dry, high-altitude site (like a mountain desert) has a “thinner” atmosphere with fewer particles to scatter light. Here, the effect of increasing air mass is less pronounced. Conversely, a humid, low-lying, or polluted area will see a much steeper performance drop with increasing air mass because the atmosphere is “thicker” with attenuating particles.
Angle of Incidence (AOI): This is often confused with air mass but is a separate geometric effect. AOI describes the angle at which light rays hit the surface of the module. When the sun is low, the AOI is large, causing more light to be reflected off the glass surface of the module. Modern modules often have anti-reflective coatings to minimize this loss. The combined effect of high air mass (spectral and irradiance loss) and a high AOI (reflection loss) is what causes the significant power reduction seen during sunrise and sunset.
Practical Implications for System Design and Energy Yield
Understanding air mass is not just academic; it has direct consequences for predicting the energy production of a solar power plant.
Energy Modeling: Sophisticated software like PVsyst or SAM (System Advisor Model) incorporate air mass algorithms to accurately simulate daily and seasonal energy yield. They use typical meteorological year (TMY) data, which includes sun position and atmospheric data, to calculate the air mass for every hour of the year. This allows designers to predict that a system in Norway (high latitude, low average sun angle) will have a different performance profile than one in Ecuador (low latitude, high average sun angle), even if both experience the same peak sun hours.
Tilt and Tracking: To maximize energy harvest, system designers adjust the tilt angle of fixed-tilt arrays to optimize the annual exposure, effectively minimizing the average air mass the array experiences. More advanced systems use single-axis or dual-axis trackers that follow the sun across the sky. By keeping the sun nearly perpendicular to the pv module for most of the day, trackers significantly reduce the effective air mass and angle of incidence losses, thereby increasing daily energy production by 20-35% compared to a fixed-tilt system.
Technology Selection: As the table indicated, the choice of module technology can be influenced by the local air mass conditions. In regions with high average air mass, a technology with a spectral response less sensitive to these shifts, like monocrystalline silicon, might have a slight advantage in predictable energy yield over the year.