Fog at the Guarulhos International Airport from 1951 to 2015

França, Gutemberg Borges; Carmo, Luiz Felipe Rodrigues do; Almeida, Manoel Valdonel de; Neto, Francisco Leite Albuquerque

doi:10.1007/s00024-018-1781-5

Cited by 6 publications

(5 citation statements)

References 19 publications

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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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confidence: 99%

Aviation Meteorology: Observations and Models. Introduction

Gültepe

Feltz

2019

Pure Appl. Geophys.

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“…Previous research focused on airport fog has yielded significant achievements (Akimoto & Kusaka, 2015; Franca et al, 2019; Roux et al, 2021; Zhou et al, 2004). However, in China, studies on airport fog remain relatively scarce, with some aspects to be improved.…”

Section: Introductionmentioning

confidence: 99%

Fog climatology at Shanghai Pudong International Airport

Hao,

Lyu,

Hao

et al. 2024

Meteorological Applications

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Using 22‐year ground observations on meteorological variables, we conducted a statistical analysis to reveal fog characteristics at Shanghai Pudong International Airport (SPIA). Fog events were classified by fog types using an objective method. Two types of advection fogs are dominant in fog events at SPIA, followed by radiation fogs. Different fogs have evident annual and monthly variations in frequency. Advection fog mostly appear from February to April, while radiation fog is mainly between November and February. Six synoptic patterns associated with fogs were determine by a self‐organizing maps cluster method, two of which, namely ‘west of marine high pressure’ and ‘inverted trough’, are most favourable for the formation of advection fogs, whereas ‘bottom of weak cold high‐pressure’ is favourable for radiation fogs. The frequency of advection fog occurrence exhibits temporal fluctuations with distinct peaks occurring 2–3 h after sunset, around midnight, and 1–2 h before sunrise, while almost all radiation fog occur in the second half of the night, and favourable conditions for fog onset become more effective with the length of the night. Radiation fog exhibits a longer duration than advection fog and tends to result in lower visibility. The prevalent wind direction for the formation of advection fog is generally from the east to the southeast, whereas radiation fog is typically associated with westerly winds. The temperature and air pressure during the formation of both advection and radiation fogs follow a normal distribution in most seasons. More fog characteristics were categorized by fog types and seasons.

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“…GIA is the primary hub for national and international airlines operating in Brazil, and its takeoff and landing operations are frequently impacted by low-visibility events. Therefore, while the aim of França et al (2018) was to diagnose fog events before and after the GIA was built, the objectives of this study are:…”

Section: Introductionmentioning

confidence: 99%

“…In a companion article to this research, França et al (2018) examined the occurrence of fog events at Guarulhos International Airport (GIA), São Paulo, Brazil, between 1951 and 2015. The following were the main ndings achieved by França et al (2018) as a result of the GIA and its surrounding urban expansion in the early 1980s:…”

Section: Introductionmentioning

confidence: 99%

Fog Forecast Model based on Machine Learning

Almeida,

França,

de Almeida

et al. 2023

Preprint

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This study introduces an innovative approach for fog forecasting based on Machine Learning (ML) algorithms. It involved utilizing eighteen years of surface and sounding meteorological data from Guarulhos International Airport and nearby Marte Airfield for training and testing ML models. Multiple categorical algorithms were trained and evaluated, with the top three models selected for further investigation. The results of the study highlight that the best-performing model, which is based on the Random Forest algorithm, can provide reasonably accurate predictions for the occurrence of fog. Specifically, it forecasts fog occurrence within a time window from 03 to 11 UTC with a reasonable degree of accuracy (Proportion Correct = 0.90 ± 0.03, Probability of detection = 0.96 ± 0.03, False Alarm Rate = 0.33 ± 0.01, Critical Success Index = 0.65 ± 0.02, and Bias = 1.43 ± 0.05). Additionally, the method indicates the most likely time for the onset and dissipation of fog events based on historical data. This research offers valuable insights into improving fog forecasting at the Guarulhos International Airport and demonstrates the potential of ML algorithms in enhancing predictive accuracy for weather-related events.

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Fog at the Guarulhos International Airport from 1951 to 2015

Cited by 6 publications

References 19 publications

Aviation Meteorology: Observations and Models. Introduction

Aviation Meteorology: Observations and Models. Introduction

Fog climatology at Shanghai Pudong International Airport

Fog Forecast Model based on Machine Learning

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