Previsão de Nevoeiro Utilizando Multicritérios Baseados em Simulações do Modelo WRF para o Aeroporto Internacional Afonso Pena

Platenik, José Eduardo Gonçalves; França, Gutemberg Borges; Neto, Antonio Vicente Pereira; Silva, Ricardo Marcelo da; Almeida, Vinícius Albuquerque de

doi:10.11137/2020_4_376_383

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…They are based on the principle that it is possible to learn from a set of training data and consequently be able to correctly classify new standards [22]. This research is part of a sequence of short-term prediction studies based on machine learning algorithms that have been carried out by the Applied Meteorology Laboratory at the Federal University of Rio de Janeiro and can be found in the work of [13,[15][16][17][18]23,24]. So, in the present study, the WEKA (Waikato Environment for Knowledge Analysis) software package (version 3.8.4) [25] developed by the University of Waikato in New Zealand was used, with and without the Auto-WEKA subsystem [26,27].…”

Section: Methodsmentioning

confidence: 99%

“…The current results of the ML algorithms show that they are capable of nowcasting convective events with a high probability and low false alarm ratio. Recently, a fog forecasting method based on the ML algorithm was developed for the Afonso Pena International Airport in Paraná, Brazil [18]. The algorithm diagnoses Vis based on 15 min observed and predicted meteorological data, from the automatic surface meteorological station and simulated data using the numerical model of Weather Research and Forecast, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Cordeiro¹,

França

Neto

et al. 2021

Atmosphere

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This work presents a novel approach for simulating visibility (Vis) and ceiling base height (Hc) in up to 1 h using several machine learning (ML) algorithms. Ten years of meteorological data at 15 min intervals for Santos Dumont airport (SDA), Rio de Janeiro, Brazil were used in the ML method training and testing process. In the investigation, several categorical and regressive algorithms were trained and tested, and the results were verified with observations. The forecast results reveal that the categorical methods produced satisfactory results only up to 15 min for visibility prediction with the probability of detection greater than 85%. On the other hand, the regressive methods were found to be more capable of generating an accurate prediction of Vis and Hc compared to categorical method up to 60 min. The forecast evaluation metrics for Vis and Hc had correlation coefficients of 0.99 ± 0.00 and 0.96 ± 0.00, with mean absolute errors of 324 ± 77 m, and 167 ± 21 m, respectively. Results suggested that ML methods can improve the prediction of Vis and Hc up to 1 h when accurate observations are used for the analysis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Cordeiro¹,

França

Neto

et al. 2021

Atmosphere

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“…In a recent study on the subject conducted by the group, a fog forecasting hybrid method was developed using the Weather Research and Forecast (WRF) model and Machine Learning (ML) algorithms for Curitiba Airport, Brazil (Platenik et al, 2020). As a result, the 24-hour visibility prediction correlation coe cient with 15-minute observation data approaches 90%.…”

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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Previsão de Nevoeiro Utilizando Multicritérios Baseados em Simulações do Modelo WRF para o Aeroporto Internacional Afonso Pena

Cited by 2 publications

References 9 publications

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Fog Forecast Model based on Machine Learning

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