Cloud and Fog Monitoring Simplified: Preliminary Validation of the Dark Channel Model with Ceilometer Observations

The impacts of global warming and urban heat island effects have significantly altered atmospheric conditions, leading to a noticeable decline in fog events which uplift to be low clouds. Understanding and monitoring these changes over the long term are crucial for climate studies and environmental management. However, identifying a practical and reliable method for monitoring cloud base height and fog patterns remains a challenge.

This study explores the use of the dark channel model, a computational approach originally developed for haze removal in images, to analyze cloud and fog dynamics. By leveraging the model's ability to estimate cloud base height and distinguish upslope     fog from other atmospheric conditions, we provide a novel method for atmospheric monitoring. The model's performance was validated using observational data from a ceilometer, an instrument known for its precision in measuring cloud base heights. The comparison revealed a strong correlation between the model's predictions and ceilometer measurements, with a coefficient of determination (R²) of 0.85.

The results demonstrate that the dark channel model is an effective tool for long-term monitoring of cloud and fog dynamics, offering both accuracy and convenience. This approach could play a pivotal role in understanding atmospheric changes in the context of climate variability and urbanization, aiding in better management and forecasting of weather and environmental conditions.