Fog Occurrence and Associated Meteorological Factors Over Kempegowda International Airport, India

Kutty, Saumya G.; Agnihotri, Geeta; Dimri, A. P.; Gültepe, Ismail

doi:10.1007/s00024-018-1882-1

Cited by 20 publications

(12 citation statements)

References 43 publications

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“…Of particular interest is the reliable forecasting of poor visibility, i.e. fog (visibility below 1 km) and mist (visibility between 1 and 5 km) [3]. For example, a dense advection-radiation fog that occurred in December 2013 led to several vehicle collisions and loss of life on a highway of the Grand Casablanca region, Morocco [4].…”

Section: Introductionmentioning

confidence: 99%

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Bari

Ouagabi

2020

SN Appl. Sci.

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Low-visibility conditions are a weather hazard that affects all forms of transport, and accurate forecasting of their spatial coverage is still a challenge for meteorologists, particularly over a large domain. Current predictions of visibility are based on physical parametrizations in mesoscale models and are thus limited with respect to accuracy. This paper examines the use of supervised machine-learning regression techniques (tree-based ensemble, feed-forward neural network and generalized linear methods) to diagnose visibility from operational mesoscale model forecasts over a large domain. To achieve this, hourly forecasts of meteorological parameters in the lower levels of the atmosphere have been used. In the short-range forecasting framework, the machine-learning algorithms were developed to provide hourly forecasts up to 24 h. To assess the performance of the developed models, hourly observed data, collected at 36 synoptic land stations over the northern part of Morocco, have been used. This region is characterized by a heterogeneous topography. The tree-based ensemble methods have shown some improvement in visibility forecasting in comparison with the operational visibility diagnostic scheme based on Kunkel's formula and also with persistence. It is also found that this machine-learning technique performs better when the forecast depends on multiple predictors instead of only a few with very high importance. In addition, their performance is very sensitive to the disproportionality of data availability between daytime and night-time. Furthermore, it is found that the performance decreases when principal components are used instead of raw correlated data.

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Section: Introductionmentioning

confidence: 99%

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Bari

Ouagabi

2020

SN Appl. Sci.

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“…This topical issue provides the current status and challenges of these systems. While none of these systems alone can provide accurate predictions of meteorological parameters over short or long-time periods, integrating these methods can be used more confidently to improve aircraft physical and managerial flying conditions, including conditions on the ground (Kutty et al 2019;França et al 2019). Several papers focusing on these issues are included in this issue.…”

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confidence: 99%

Aviation Meteorology: Observations and Models. Introduction

Gültepe

Feltz

2019

Pure Appl. Geophys.

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“…time and space and that lidar can well reflect the changes of sea fog, especially when this occurs in patches (i.e., duration in minutes). 1 Several low-visibility events also occurred during February 13-16, 2020, as shown in Fig. 4.…”

Section: Resultsmentioning

confidence: 91%

“…Fog is characterized by the suspension of very small droplets and ice crystals of water in air that results in a visibility of <1 km (5/8 of a statute mile). 1,2 Fog and its effect on visibility (Vis) have significant impacts on our daily lives. 3 In navigation, sea fog is a disastrous weather event that substantially affects the safety of ships.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and analysis of sea fog in an offshore waterway using lidar

Yang²

2021

Opt. Eng.

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Until now, sea fog monitoring relied mainly on point monitoring equipment, such as a forward scatter visibility sensor mounted on the shore or on an island, which produce non-ideal data during a variable and unevenly distributed sea fog. In 2019, a visibility lidar instrument for sea fog monitoring was installed in the Beilun area of the Ningbo Zhoushan Port, China. Two sea fog events were monitored in February 2020, and the data from the lidar were compared with those from a forward scatter visibility sensor. The results show that the visibility lidar instrument is advantageous for sea fog monitoring, and the correlation of the lidar instrument data with the forward scatter sensor data demonstrates the utility and potential of the lidar for sea fog detection.

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Fog Occurrence and Associated Meteorological Factors Over Kempegowda International Airport, India

Cited by 20 publications

References 43 publications

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Machine-learning regression applied to diagnose horizontal visibility from mesoscale NWP model forecasts

Aviation Meteorology: Observations and Models. Introduction

Monitoring and analysis of sea fog in an offshore waterway using lidar

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