Fog in Sofia 2010–2019: Objective Circulation Classification and Fog Indices

Abstract

Low visibility caused by fog events can lead to disruption of every type of public transportation, and even loss of life. The focus of this study is the synoptic conditions associated with fog formation. The data used in this study was collected over the course of ten years (2010–2019) in Sofia, Bulgaria. The forecast skills of the Fog Stability Index (FSI) and the local Sofia Stability Index (SSI), as well as the relation between the Integrated Water Vapor (IWV) and fog from the Global Navigation Satellite System (GNSS), were tested. Both fog indices are used for fog nowcasting as their lead times are short and unclear. The Jenkinson–Collison Type method was used for extracting the predominant synoptic-scale pressure systems which provide suitable weather conditions for fog formation. Surface observations from two synoptic stations were used to calculate and evaluate the performance of the two fog indices and of the ground-based GNSS receiver for the IWV. The forecast skills provided by Probability of Detection (POD) and False Alarm Ratio (FAR), for both fog and no-fog periods, were obtained by discriminant analysis. Additionally, several weather parameters, such as surface wind speed, relative humidity and IWV, were added in order to improve the results of the local index (SSI). This led to a 77.9% hit rate. The cyclonic system influence and zonal flows from the west and the southwest are both responsible for a number of fog cases that are comparable to those associated with the anticyclonic system. The IWV was not found to improve the forecast skill of the fog indices. However, it was found that its values had a larger spread during no-fog periods in comparison to fog periods.

Sofia Airport Visibility Estimation with Two Machine-Learning Techniques

“…Whilst the majority of cases reported are above a pressure of 1013 hPa, cases reported below that value are not negligible. While the majority of fog observations are associated with anticyclonic weather, there are a number of fog cases caused by an approaching cyclone from W-SW or a low-gradient pressure field and a weak flow at 850 hPa pressure level from W-SW, as reported in Penov et al [15]. Figure 3d The frequency of fog observations (Figure 4) for the period 2005-2022 is divided into three consecutive periods of five years and one period of three years (2005-2009, 2010-2014, 2015-2019, and 2020-2022).…”

“…The index has been in operational use since 2019 at the National Institute of Meteorology and Hydrology in Bulgaria. Penov et al [15] used both the FSI and the SSI to evaluate their capability for fog detection in Sofia, Bulgaria. The SSI was improved by adding wind speed, relative humidity, and integrated water vapor.…”

Fog Decision Support Systems: A Review of the Current Perspectives

“…(1) Integration of Machine Learning: DSSs could incorporate machine learning algorithms to analyze large volumes of historical and real-time data, complementing other, more traditional, algorithms, such as calculating fog indices (see, e.g., [29]). This could enhance the identification of patterns and relationships between weather conditions and the likelihood of fog formation; (2) Improved data collection: The accuracy of fog forecasting can be improved by collecting more comprehensive data.…”

Study on Risk Prediction Model of Expressway Agglomerate Fog-Related Accidents

“…Using a ground-based counterflow virtual impactor, Duplessis et al [13] measured the size distributions of fog droplet and aerosol near Halifax on the eastern coast of Canada, as well as the fog droplet residuals. In addition, many studies analyzed the macro characteristics related to the formation, development and dissipation of fog, such as the synoptic pattern and meteorological factors (wind speed, relative humidity and moisture) [14][15][16].…”