A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe

Egli, Sebastian; Thies, Boris; Bendix, Jörg

doi:10.1002/qj.3522

Cited by 10 publications

(8 citation statements)

References 42 publications

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“…These definition differences could impact relationships found between fog and WDs. Other approaches could be employed to determine relationships between weather type and fog such as using weather patterns (Neal et al ., 2020) or satellite derived fog datasets (Egli et al ., 2019).…”

Section: Discussionmentioning

confidence: 99%

Fog trends in India: Relationships to fog type and western disturbances

Smith

Dorling

Renfrew

et al. 2022

Intl Journal of Climatology

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Fog is a major hazard in wintertime over India, particularly in the Indo-Gangetic Plains, leading to significant impacts for transport and human health. Using 3-hourly surface observations, from 69 sites across India, all fog and dense fog events between 2000 and 2020 are identified. For each event, the main fog formation mechanism is objectively categorized using a classification algorithm, distinguishing between radiation, advection, evaporation, precipitation or cloud-base lowering fog types. In contrast to the findings of other international studies, radiation fog dominates as the most common fog type at the vast majority of locations in India, accounting for 68.1% of all fog events and 70.0% of dense fog events. Statistically significant positive trends are seen in the frequency of all fog events at Delhi, Lucknow and Patna, in the Indo-Gangetic plains, between 1997/1998 and 2018/2019, dominated by comparable statistically significant positive trends in radiation fogs. Western disturbances (WD) are often linked to the formation of fog in India. Using a climatology of WDs, we show that 46.9% of radiation fog onsets in Delhi in December and January, the primary fog months, happened in conjunction with an active WD event. Conversely, only 32.3% of WDs during these same months coincided with the onset of a radiation fog event. WD-related radiation fog events are shown to cluster into three distinct groups, with WD centres located to the northwest (51.4% of cases), southwest (13.3%) and east (35.2%) of Delhi. Each cluster is shown to have coherent and distinct near-surface characteristics which are conducive to fog formation. Trends in WD frequency cannot fully account for the observed trends in fog events. We argue that the fog trends are more likely the result of a complex interaction between urban expansion and the associated rapid change in aerosol loading, resulting in impacts on radiation balance, microphysics and heat-island processes.

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Section: Discussionmentioning

confidence: 99%

Fog trends in India: Relationships to fog type and western disturbances

Smith

Dorling

Renfrew

et al. 2022

Intl Journal of Climatology

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“…In order to facilitate the analysis, satellite‐derived climatologies, such as presented by Egli et al . () would be extremely useful.…”

Section: Discussionmentioning

confidence: 99%

“…More recently, Egli et al . () presented an analysis of satellite‐derived fog occurrence over Europe, focusing on the continental‐scale patterns of fog distribution, which are heavily influenced by topography. In a much smaller area, Cereceda et al .…”

Section: Introductionmentioning

confidence: 99%

Dutch fog: On the observed spatio‐temporal variability of fog in the Netherlands

Izett

Wiel

Baas

et al. 2019

Quart J Royal Meteoro Soc

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The Netherlands is characterized by highly variable land use within a small area, and a strong influence of the North Sea on national climate. Devoid of significant topography, it is an excellent location for assessing the relative influence of various factors on fog occurrence in the absence of terrain effects. Using observations from a dense network of weather stations throughout the country, the climatology of fog in the Netherlands is assessed over a period of 45 years. On a national scale, interannual variability is linked to changes in synoptic pressure‐gradient forcing. Within the country, a comprehensive in‐depth analysis of regional differences between fog occurrence is made, together with an assessment of local physical factors which could bias fog formation in one location over another. Regional variability is shown to be strongly related to the mesoscale influences of urbanization and the North Sea. In fact, some locations experience over twice as much fog as others. From this finding, a simple index is presented, which combines the water and urban fraction surrounding a station. This “Regionally Weighted Index” (RWI) is able to accurately sort the stations according to their relative fogginess. Its practical use is encouraged for assessing a given site's climatological favourability, even when in situ meteorological observations are unavailable.

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“…On the other hand, as can be seen in Figure 10b, other extreme conditions only affect areas with higher elevations like the lower mountain ranges. The fog patterns P8 and P13 in Figure 10a,b are based on a self-organizing map approach assigning daily averaged fog frequency maps to specific fog patterns which is presented in [24]. Figure 10c ("high over central Europe" (HM) weather type) and Figure 10d ("anticyclonic southwesterly" (SWA) weather type) show the average availabilities of two general weather situations (GWS), based on the work in [27], commonly associated with favorable conditions for lowland radiative fog.…”

Section: Spatial Availability Distribution With Regard To Weather Patternsmentioning

confidence: 99%

“…Weather satellites have long been used successfully to detect the presence of fog and low stratus [19][20][21][22], and therefore can give useful information about FLS on high spatiotemporal scales. A newly developed scheme based on machine learning technology called HYFOG allows the spatially explicit calculation of ground fog, based on the retrieval of FLS cloud base altitudes (CBA) for the entire area of Europe from data obtained from the Meteosat Second Generation (MSG) SEVIRI (Spinning Enhanced Visible and Infrared Imager) instrument [23,24].…”

Section: Introductionmentioning

confidence: 99%

Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning

et al. 2020

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Coherent wind doppler lidar (CWDL) is a cost-effective way to estimate wind power potential at hub height without the need to build a meteorological tower. However, fog and low stratus (FLS) can have a negative impact on the availability of lidar measurements. Information about such reductions in wind data availability for a prospective lidar deployment site in advance is beneficial in the planning process for a measurement strategy. In this paper, we show that availability reductions by FLS can be estimated by comparing time series of lidar measurements, conducted with WindCubes v1 and v2, with time series of cloud base altitude (CBA) derived from satellite data. This enables us to compute average maps (2006–2017) of estimated availability, including FLS-induced data losses for Germany which can be used for planning purposes. These maps show that the lower mountain ranges and the Alpine regions in Germany often reach the critical data availability threshold of 80% or below. Especially during the winter time special care must be taken when using lidar in southern and central regions of Germany. If only shorter lidar campaigns are planned (3–6 months) the representativeness of weather types should be considered as well, because in individual years and under persistent weather types, lowland areas might also be temporally affected by higher rates of data losses. This is shown by different examples, e.g., during radiation fog under anticyclonic weather types.

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A spatially explicit and temporally highly resolved analysis of variations in fog occurrence over Europe

Cited by 10 publications

References 42 publications

Fog trends in India: Relationships to fog type and western disturbances

Fog trends in India: Relationships to fog type and western disturbances

Dutch fog: On the observed spatio‐temporal variability of fog in the Netherlands

Fog and Low Stratus Obstruction of Wind Lidar Observations in Germany—A Remote Sensing-Based Data Set for Wind Energy Planning

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