Short-range forecasting system for meteorological convective events in Rio de Janeiro using remote sensing of atmospheric discharges

Almeida, Vinícius Albuquerque de; França, Gutemberg Borges; Velho, Haroldo Fraga de Campos

doi:10.1080/01431161.2020.1717669

Cited by 9 publications

(4 citation statements)

References 26 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%

“…Similarly, a fog prediction method based on a ML algorithm was developed for the Brazilian Air Force aerodrome at Pirassununga using meteorological observation data collected from 1989 to 2008, and it was concluded that the suggested neural network algorithm predictions are 95 percent equivalent to observations [14]. A series of works [15][16][17], explored the use of ML algorithms for short-term forecasting of convective events for the Rio de Janeiro metropolitan region. 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.…”

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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“…The present article is part of a sequence of studies related to nowcasting under development and implementation by the Laboratory for Applied Meteorological at the Federal University of Rio de Janeiro, following Almeida (2009), Silva et al (2016), França et al (2018), and Almeida et al (2020. All these studies encompass researches based on artificial intelligence methods for weather forecasts, mainly for high-impacting phenomena for aviation.…”

Section: Introductionmentioning

confidence: 99%

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Almeida

Velho²,

França³

et al. 2022

Preprint

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Abstract. The practical feasibility of neural networks models for data assimilation using local observations data in the WRF model for the Rio de Janeiro metropolitan region in Brazil is evaluated. Surface and multi-level variables retrieved from airport meteorological stations are used: air temperature, relative humidity, and wind (speed and direction). Also, 6-hour forecast from WRF high-resolution simulations are used – domain centered in the Rio de Janeiro city with nested grids of 8 and 2.6 km. Periods of 168 h from 2015–2019 are used with 6 h and 12 h assimilation cycles for surface and upper-air data, respectively, applied to 6-hour forecast fields. The observed data (interpolated to grid points close to airport locations and influence computed in its surroundings) and short-range forecasts are used as input for training model and the 3D-Var analysis on 6-hour forecast fields for each grid point is used as target variable. The neural network models are built using two different approaches: WEKA mul- tilayer perceptron model and TensorFlow’s deep learning implementation. The year of 2019 is used as an independent dataset for forecast validation from the trained models. Results employing 6-hour forecast fields with neural network models are able to emulate the 3D-Var results for surface and multi-level variables, with better results for the NN-TensoFlow implementation. The main result refers to CPU time reduction enabled by the neural networks models, reducing the data assimilation CPU-time by 121 times and 25 times for NN-TensorFlow and NN-WEKA, respectively, in comparison to the 3D-Var method under the same hardware configurations.

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“…The present article is part of a sequence of studies related to nowcasting that have been executed by the Applied Meteorological Laboratory at the Federal University of Rio de Janeiro, following Almeida (2009), Silva et al (2016), França, Almeida, and Rossete (2016), França et al (2018), Paulucci et al (2019, and Almeida et al (2020aAlmeida et al ( , 2020b. All these studies encompass researches based on artificial intelligence and methods of limited-area numerical weather forecasts.…”

Section: Introductionmentioning

confidence: 99%

Data Assimilation Using WRFDA Over the Terminal Area of Rio de Janeiro

Almeida

França

Velho

et al. 2021

Rev. bras. meteorol.

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The impact of the data assimilation process of air temperature and relative humidity from surface meteorological stations and sounding at airports in the terminal area of Rio de Janeiro is evaluated using the Weather Research and Forecast Data Assimilation system. Synthetic data of temperature, relative humidity and wind are generated in the locations of airport sensors by applying a white-noise perturbation in the forecast data. Results show a positive overall impact of the assimilation process with the removal of part of the noise in the observation data but keeping the effect of local conditions in the later timesteps of the simulation. In addition, with the assimilation process there is a global reduction of the error between the analysis data and the observation data. In the future, a neural network will be trained to emulate the data assimilation process to speed-up the assimilation process in the WRF model.

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Short-range forecasting system for meteorological convective events in Rio de Janeiro using remote sensing of atmospheric discharges

Cited by 9 publications

References 26 publications

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Visibility and Ceiling Nowcasting Using Artificial Intelligence Techniques for Aviation Applications

Neural networks for data assimilation of surface and upper-air data in Rio de Janeiro

Data Assimilation Using WRFDA Over the Terminal Area of Rio de Janeiro

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