Abstract
Abstract. Visibility reduction caused by fog can be hazardous for human activities, especially for the transport sector. Previous studies show that this problem could be mitigated by improving nowcasting of fog dissipation. To address this issue, we propose a new paradigm which could potentially improve our understanding of the life cycle of adiabatic continental fogs and of the conditions that must take place for fog dissipation. For this purpose, adiabatic fog is defined as a layer filled with suspended liquid water droplets, extending from an upper boundary all the way down to the surface, with a saturated adiabatic temperature profile. In this layer, the liquid water path (LWP) must exceed a critical value: the critical liquid water path (CLWP). When the LWP is less than the CLWP, the amount of fog liquid water is not sufficient to extend all the way down to the surface, leading to a surface horizontal visibility greater than 1 km. Conversely, when the LWP exceeds the CLWP, the amount of cloud water is enough to reach the surface, inducing a horizontal visibility of less than 1 km. The excess water with respect to the critical value is defined as the reservoir liquid water path (RLWP). The new fog paradigm is formulated as a conceptual model that relates the liquid water path of adiabatic fog with its thickness and surface liquid water content and allows the critical and reservoir liquid water paths to be computed. Both variables can be tracked in real time using vertical profiling measurements, enabling a real-time diagnostic of fog status. The conceptual model is tested using data from 7 years of measurements performed at the SIRTA observatory, combining cloud radar, microwave radiometer, ceilometer, scatterometer, and weather station measurements. In this time period we found 80 fog events with reliable measurements, with 56 of these lasting more than 3 h. The paper presents the conceptual model and its capability to derive the LWP from the fog top height and surface horizontal visibility with an uncertainty of 10.5 g m−2. The impact of fog liquid water path and fog top height variations on fog life cycle (formation to dissipation) is presented based on four case studies and statistics derived from 56 fog events. Our results, based on measurements and an empirical parametrization for the adiabaticity, validate the applicability of the model. The calculated reservoir liquid water path is consistently positive during the mature phase of fog and starts to decrease quasi-monotonously about 1 h before dissipation, reaching a near-zero value at the time of dissipation. Hence, the reservoir liquid water path and its time derivative could be used as indicators of the life cycle stage, to support nowcasting of fog dissipation.
Highlights
Fog occurs due to multiple processes that lead to water vapor saturation in the air close to the surface
This motivated the definition of a critical liquid water path (CLWP), which is the minimum amount of LWP needed for a cloud to reach the surface and reduce horizontal visibility below 1000 m
We have a fog with a given cloud top height CTH and a liquid water content LWP, which are associated with a liquid water content LWC0 at the surface
Summary
Fog occurs due to multiple processes that lead to water vapor saturation in the air close to the surface. Cermak and Bendix (2011) define fog and stratiform clouds based on cloud layer top altitude and liquid water content that follows a sub-adiabatic profile. A fog adiabatic layer is defined as a stratiform cloud that contains sufficient liquid water to reach down to the surface. Using a large eddy-simulation model and remote-sensing measurements, Wærsted et al (2019) showed that dissipation of fog can occur due to both reduction in liquid water content of the fog layer and increase in fog top height. In this article we present a conceptual model that relates the liquid water path of adiabatic fog to its geometrical thickness and surface liquid water content. 4 we derive a parametrization for fog adiabaticity using historical data, and we compare the conceptual model predictions with fog thickness, liquid water path, and surface liquid water content observations.
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