Understanding Cloud Coverage Using Satellite Data: A Case Study from Fujairah

Clouds play an important role in regulating Earth’s climate and weather. They can reflect sunlight back into space or trap heat near the surface, influencing temperature, rainfall, and atmospheric conditions. Because of this, understanding cloud coverage is essential for accurate weather forecasting and climate monitoring.

In the past, cloud observation depended largely on human judgment, which could vary from one observer to another. Today, satellite technology combined with machine learning offers a more accurate and consistent way to study cloud patterns over large areas.

Satellite data from the Sentinel-3 mission is widely used to observe Earth’s surface and atmosphere. One key measurement provided by this satellite is Land Surface Temperature (LST), which helps scientists understand how heat moves between the land, air, and vegetation. Changes in surface temperature can also indicate the presence and type of clouds.

By analyzing satellite images using a machine learning technique known as K-means clustering, cloud coverage can be automatically grouped into clear categories. These include areas without clouds, rainy clouds, moisture-rich clouds, dry clouds, and regions where data is unavailable. Each category is represented visually using different colors, making cloud patterns easy to identify and interpret.

This approach allows cloud types to be monitored over time. Observations from different months show that cloud coverage changes throughout the year, influenced by seasonal weather conditions. Some months experience more rain-bearing clouds, while others are dominated by dry or moisture-only clouds.

The accuracy of this automated method has been tested by comparing results with human observations. The findings show very high accuracy for both clear and cloudy conditions, demonstrating that machine learning can reliably identify cloud types using satellite data. Reference

Fujairah Research Centre