A 10 year fog and low stratus climatology for Europe based on <i>Meteosat</i> Second Generation data

Egli, Sebastian; Thies, Boris; Drönner, Johannes; Čermák, Jan; Bendix, J.

doi:10.1002/qj.2941

Cited by 43 publications

(82 citation statements)

References 51 publications

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“…Also, since fog only prevails where an FLS patch actually touches the ground and since CBAs are relatively homogeneous within an FLS patch, fog usually only occurs at the margins of FLS patches where the terrain height increases. The fog map is therefore almost an inverted version of the FLS map that was presented in Egli et al [20] where lowlands and river valleys are prone to higher FLS frequencies than their neighbouring mountain ranges.…”

Section: Fog Climatologymentioning

confidence: 99%

“…This is exploited in many ecological studies, e.g., Wijaya et al [33] or Schulz et al [34]. As spatial information in the form of standard deviations within cloud patches is also included in the FLS detection algorithms of Cermak and Bendix [18] and Egli et al [20], it seemed promising to also provide this information to an ML model to improve the prediction of CBA values. Thus, several geostatistical texture features of the 5 × 5 pixel surroundings of each METAR station were provided for the model training.…”

Section: Preprocessing and Input Featuresmentioning

confidence: 99%

“…Unfortunately, all satellite-based approaches suffer from the same insufficiency as they can only provide information from the top perspective. The approach of Cermak and Bendix [18] and Egli et al [20] thus did not provide data that distinguished between fog and elevated low stratus layers.…”

Section: Introductionmentioning

confidence: 99%

“…They applied the scheme to the winter months (December-February) between 2004 and 2008 [19]. The original algorithms were later adapted by Egli et al [20] to make them suitable for varying conditions in different domain regions and for seasonal changes. Unfortunately, all satellite-based approaches suffer from the same insufficiency as they can only provide information from the top perspective.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

2018

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Fog has a substantial influence on various ecosystems and it impacts economy, traffic systems and human life in many ways. In order to be able to deal with the large number of influence factors, a spatially explicit high-resoluted data set of fog frequency distribution is needed. In this study, a hybrid approach for fog retrieval based on Meteosat Second Generation (MSG) data and ground truth data is presented. The method is based on a random forest (RF) machine learning model that is trained with cloud base altitude (CBA) observations from Meteorological Aviation Routine Weather Reports (METAR) as well as synoptic weather observations (SYNOP). Fog is assumed where the model predicts CBA values below a dynamically derived threshold above the terrain elevation. Cross validation results show good accordance with observation data with a mean absolute error of 298 m in CBA values and an average Heidke Skill Score of 0.58 for fog occurrence. Using this technique, a 10 year fog baseline climatology with a temporal resolution of 15 min was derived for Europe for the period from 2006 to 2015. Spatial and temporal variations in fog frequency are analyzed. Highest average fog occurrences are observed in mountainous regions with maxima in spring and summer. Plains and lowlands show less overall fog occurrence but strong positive anomalies in autumn and winter.

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Section: Fog Climatologymentioning

confidence: 99%

Section: Preprocessing and Input Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

2018

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“…Although the annual number of low-visibility events over central Europe has declined during the last 40 years (e.g. Egli et al 2017;Vautard et al 2009), the number of delays has increased. The reason is the increased number of flights (Federal Aviation Administration 2017), which exhaust the capacity utilization of airports.…”

Section: Discussionmentioning

confidence: 99%

Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods

Dietz

Kneringer

Mayr

et al. 2018

Pure Appl. Geophys.

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Abstract-Low-visibility conditions at airports can lead to capacity reductions and, therefore, to delays or cancelations of arriving and departing flights. Accurate visibility forecasts are required to keep the airport capacity as high as possible. We generate probabilistic nowcasts of low-visibility procedure (lvp) states, which determine the reduction of the airport capacity due to low visibility. The nowcasts are generated with tree-based statistical models based on highly-resolved meteorological observations at the airport. Short computation times of these models ensure the instantaneous generation of new predictions when new observations arrive. The tree-based ensemble method boosting provides the highest benefit in forecast performance. For lvp forecasts with lead times shorter than 1 h variables with information of the current lvp state, ceiling, and horizontal visibility are most important. With longer lead times, visibility information of the airport's vicinity and standard meteorological variables such as humidity also become relevant.

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Climatology of fog occurrence over a wide flat area in Serbia based on visibility observations

Veljović

Vujović

2018

Intl Journal of Climatology

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Long‐term analysis of occurrence of fog events at 14 stations across part of fairly flat terrain of Serbia, which includes the area of the Pannonian Plain, is conducted. For the majority of stations (Palić, Novi Sad, Kikinda, Vršac, Belgrade‐Surčin, Smederevska Palanka, Valjevo, Loznica, Negotin) data were available for the 33‐year period, 1973–2005. For the rest of stations, data were available for shorter periods: for Sombor and Veliko Gradište 1982–2005, for Sremska Mitrovica and Belgrade‐Vračar 1983–2005, and for Zrenjanin 1989–2005. Fog event is defined only based on visibility observations. Almost 75% of analysed data sets showed a negative annual trend of low‐visibility events throughout the analysed time periods. This negative trend is increasing as it goes towards the north of Serbia and most of the stations with decreasing trend are rural. Fog is most frequent in the period between October and February and it is likely mostly due to radiative cooling and especially in October when an anticyclonic synoptic situation is prevailing. Stations with higher elevations have more lasting fog events and fog events with more variable mean relative humidity compared to their counterparts, which ranges from 92.1–97.4%. For most of stations December is the month with the highest frequency of occurrence of fog in the presence of a low cloud. Frequency of fog onset during low wind speed (below 2 m/s) is analysed along with its correlation with a frequency of fog onset when the cloud base height is equal to or above 2,000 m. In ~71% of the stations correlations are higher than 0.6, and stations with a lower elevations show a generally better correlation than those with higher elevations.

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A 10 year fog and low stratus climatology for Europe based on Meteosat Second Generation data

Cited by 43 publications

References 51 publications

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

A Hybrid Approach for Fog Retrieval Based on a Combination of Satellite and Ground Truth Data

Forecasting Low-Visibility Procedure States with Tree-Based Statistical Methods

Climatology of fog occurrence over a wide flat area in Serbia based on visibility observations

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