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Leveraging Integrated Water Vapor derived from GPS for fog detection and fog characteristics analysis

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This study characterizes the physical processes involved in fog formation through an analysis of Integrated Water Vapor (IWV) measured via GPS, combined with in situ meteorological data, with a particular focus on atmospheric humidity. The research was conducted in the Casablanca-Nouaceur airport, Morocco, where fog events present significant challenges to air transportation by severely reducing visibility. In this study, fog is modeled as a simple function of water vapor and cloud water within the framework of a non-precipitating warm cloud based on the bulk water-continuity model, with the assumption that the atmosphere above the fog layer remains horizontally homogeneous during fog events.The objectives were to investigate the relationship between IWV and meteorological conditions during the whole lifecycle of fog events and to evaluate the potential of IWV for detecting and classifying these events. Over a six-year period (2017–2022), 207 fog episodes were analyzed in terms of occurrence, duration, intensity, and seasonal variability. The effectiveness of IWV as an indicator for fog detection and classification was also assessed. Using a fog type classification algorithm, the results revealed that that the most prevalent fog types were advection-radiation fog and stratus-lowering fog, followed by radiation fog and, less frequently, advection fog. These episodes exhibited strong seasonal variability, with higher occurrence rates during winter and autumn, corresponding to favorable meteorological conditions such as low temperatures and high relative humidity. Fog formation typically occurred during nighttime or early morning hours, dissipating gradually after sunrise.Analysis of IWV variations before and during fog episodes revealed a consistent increase in IWV prior to fog formation, signaling an influx of moisture conducive to condensation. During the mature fog phase, IWV remained relatively stable, while the mixing ratio in the lower atmospheric layers decreased, indicating active condensation processes. These trends varied across different fog types, highlighting the complexity of the thermodynamic interactions involved. However, attempts to classify fog types based solely on IWV—using parameters such as IWV magnitude at fog onset, its amplitude during the three hours preceding formation, and its trend—proved challenging. The results demonstrate that GPS-derived IWV is a valuable tool for detecting changes in atmospheric moisture dynamics associated with fog events. However, its capacity for classifying specific fog types is limited due to the influence of additional factors, including temperature, wind patterns, and surface atmospheric conditions, which IWV measurements alone cannot capture.Keywords : fog; GPS IWV; visibility; fog classification; water vapour; cloud water
Title: Leveraging Integrated Water Vapor derived from GPS for fog detection and fog characteristics analysis
Description:
This study characterizes the physical processes involved in fog formation through an analysis of Integrated Water Vapor (IWV) measured via GPS, combined with in situ meteorological data, with a particular focus on atmospheric humidity.
The research was conducted in the Casablanca-Nouaceur airport, Morocco, where fog events present significant challenges to air transportation by severely reducing visibility.
In this study, fog is modeled as a simple function of water vapor and cloud water within the framework of a non-precipitating warm cloud based on the bulk water-continuity model, with the assumption that the atmosphere above the fog layer remains horizontally homogeneous during fog events.
The objectives were to investigate the relationship between IWV and meteorological conditions during the whole lifecycle of fog events and to evaluate the potential of IWV for detecting and classifying these events.
Over a six-year period (2017–2022), 207 fog episodes were analyzed in terms of occurrence, duration, intensity, and seasonal variability.
The effectiveness of IWV as an indicator for fog detection and classification was also assessed.
Using a fog type classification algorithm, the results revealed that that the most prevalent fog types were advection-radiation fog and stratus-lowering fog, followed by radiation fog and, less frequently, advection fog.
These episodes exhibited strong seasonal variability, with higher occurrence rates during winter and autumn, corresponding to favorable meteorological conditions such as low temperatures and high relative humidity.
Fog formation typically occurred during nighttime or early morning hours, dissipating gradually after sunrise.
Analysis of IWV variations before and during fog episodes revealed a consistent increase in IWV prior to fog formation, signaling an influx of moisture conducive to condensation.
During the mature fog phase, IWV remained relatively stable, while the mixing ratio in the lower atmospheric layers decreased, indicating active condensation processes.
These trends varied across different fog types, highlighting the complexity of the thermodynamic interactions involved.
However, attempts to classify fog types based solely on IWV—using parameters such as IWV magnitude at fog onset, its amplitude during the three hours preceding formation, and its trend—proved challenging.
The results demonstrate that GPS-derived IWV is a valuable tool for detecting changes in atmospheric moisture dynamics associated with fog events.
However, its capacity for classifying specific fog types is limited due to the influence of additional factors, including temperature, wind patterns, and surface atmospheric conditions, which IWV measurements alone cannot capture.
Keywords : fog; GPS IWV; visibility; fog classification; water vapour; cloud water.

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