Researchers Shed Light on How Mountain Fog Is Formed

Fog is a near-surface cloud formed when water vapor condenses into microscopic liquid droplets or ice crystals suspended in the air, reducing horizontal visibility to less than one kilometer. Although it appears deceptively simple, fog is one of the most complex atmospheric phenomena to observe, model, and forecast. Its formation depends on tightly coupled interactions between temperature, humidity, aerosols, surface properties, and atmospheric dynamics within the lowest portion of the atmosphere—the boundary layer.

Despite major advances in numerical weather prediction, fog forecasting remains less reliable than forecasts for precipitation, wind, or convective storms. This gap has significant societal consequences. Reduced visibility caused by fog disrupts aviation, road transport, and marine operations, contributing to thousands of fatalities worldwide each year.

Mountain fog and valley fog are especially challenging. Complex terrain modifies airflow, traps cold air, and creates strong temperature inversions that conventional weather models struggle to resolve. Understanding how fog is formed under these conditions, and how it evolves over time, is essential for improving predictive skill and mitigating safety risks.

How is fog formed? Key physical processes

Fog forms when air becomes saturated, meaning the air temperature approaches the dew point temperature. Saturation can occur through several mechanisms:

• Radiative cooling of the surface and adjacent air
• Advection of warm, moist air over a cooler surface
• Mixing of air masses with different temperatures and humidity
• Evaporation from water bodies or moist surfaces

Once saturation is reached, water vapor condenses onto aerosol particles known as cloud condensation nuclei, forming droplets typically 1–20 µm in diameter.

Boundary layer meteorology is central to fog formation. The atmospheric boundary layer, also known as the planetary boundary layer, is the lowest part of the atmosphere, directly influenced by surface heating, cooling, friction, and moisture exchange. At night, radiative cooling often produces a stable boundary layer, suppressing turbulence and trapping moisture near the ground—ideal conditions for fog development.

In complex terrain, these effects are amplified. Slopes, valleys, and land–water contrasts create localized circulations that strongly influence temperature and humidity distributions.

Fog in complex terrain

Mountain fog (Table 1) is particularly difficult to predict because terrain modifies airflow at very fine spatial scales. One of the most important processes is cold-air drainage, where dense, cooled air flows downslope at night and accumulates in valley bottoms. This pooling of cold air creates temperature inversions that can persist for hours or days.

Valleys bordered by high terrain and moisture sources, such as rivers or reservoirs, are especially prone to fog. The combination of trapped cold air, limited mixing, and abundant moisture leads to persistent fog episodes.

Table 1: Common fog types in mountain valleys.

Numerical weather prediction and its limitations

Today, most forecasting utilizes numerical weather prediction (NWP), which processes massive meteorological observations using computer models to output predictions for precipitation, temperature, and various other elements of the weather. These models ingest massive volumes of observational data—from satellites, weather stations, radiosondes, and radar—to generate forecasts.

While NWP has achieved remarkable accuracy for large-scale phenomena, fog remains problematic. The reasons are largely technical:

• Fog forms at scales smaller than the typical model grid spacing
• Turbulence and radiation are difficult to parameterize in stable conditions
• Microphysical processes occur close to the surface
• Terrain effects require very high spatial resolution

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High-resolution fog modeling demands grid spacing on the order of tens of meters and time steps of seconds. Such simulations are computationally expensive, limiting their use in operational forecasting. Improving fog prediction, therefore, requires not only better physics but also better observational constraints to inform model development.

Field observations as a path forward

Studying fog in a mountain valley environment

One approach to improving fog forecasting is intensive field observation in representative environments. A multi-institutional effort led by atmospheric scientists focused on cold fog in a northern Utah mountain basin as part of the Cold Fog Amongst Complex Terrain project.

The study area, Heber Valley, lies southeast of Salt Lake City and is surrounded by high terrain and reservoirs that provide a persistent moisture source. These characteristics make it a natural laboratory for studying cold-air pool fog.

Instrumentation and measurement techniques

To capture the full fog life cycle, researchers deployed a dense observational network combining in situ and remote-sensing platforms. The researchers set up two major data-collecting stations, one near Deer Creek Reservoir and another a few miles up the Provo River. These are low spots in the valley, about 5,450 feet above sea level, that see the densest fog. These sites were equipped with 100-foot towers to support an array of instruments that captured various meteorological data associated with humidity, wind, visibility, temperature, even snow depths, and soil moisture. Additionally, the team recorded a lesser array of data points at nine satellite sites.

Insights into cold-air pool fog

Observations revealed that cold-air pool fog often forms shortly before sunrise, when surface temperatures are lowest, and turbulence is minimal. Once established, the fog layer can persist well into the day if solar heating is insufficient to erode the inversion.

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Non-fog observation periods were equally valuable, offering insight into inversion formation, ice crystal development, and moisture transport—all key precursors to fog events.

“Fog involves a lot of physics processes so it requires a computer model that can better represent all these processes,” said Dr. Zhaoxia Pu, a professor of atmospheric sciences at the University of Utah. “Because fog is clouds near the ground, it requires a high-resolution model to resolve it, so we need models at a very fine scale, which are computationally very expensive. The current models (relatively coarser in resolution) are not capable of resolving the fog processes, and we need to improve the models for better fog prediction.”

Implications for fog forecasting

Improved understanding of fog processes over complex terrain has direct implications for forecasting systems. High-quality observational datasets enable researchers to:

• Evaluate and refine boundary layer parameterizations
• Improve the representation of surface energy balance
• Constrain fog microphysics in models
• Develop probabilistic fog forecasts

In the long term, these advances could lead to operational models capable of resolving fog-prone environments with greater accuracy, improving safety for aviation and ground transportation.

Looking ahead to better fog prediction

Fog remains one of the last frontiers of operational weather forecasting. Its sensitivity to small-scale processes, particularly in mountain environments, challenges both observational capabilities and numerical models. However, coordinated field campaigns, advanced sensing technologies, and continued improvements in numerical weather prediction offer a path forward.

By treating fog as a boundary layer phenomenon shaped by terrain, moisture, and microphysics, atmospheric scientists are gradually closing the gap between observation and prediction.

This article is a rework of a press release issued by the University of Utah. Material has been edited for length and the content has been updated to provide additional context and details of related developments since the original press release was published on our website. This content includes text that has been created with the assistance of generative AI and has undergone editorial review before publishing. Technology Networks' AI policy can be found here.