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

2020; Taylor & Francis; Volume: 41; Issue: 11 Linguagem: Inglês

10.1080/01431161.2020.1717669

1366-5901

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

Precipitation Measurement and Analysis

In this study, a method is presented for meteorological convective event forecasting at the terminal control area of the Galeão International Airport, Rio de Janeiro, Brazil, using machine learning, sounding and remotely sensed atmospheric discharge data from 2001 to 2016. A monthly and daily climatology were computed for the atmospheric discharge temporal distribution in the study area. Six machine learning models were trained and cross-validated for 10 years (2001–2010), and a test was produced for 6 years (2011–2016). The results showed that the deep learning fully-connected (dense) algorithm achieved the best results for storm forecast and severity based on the following statistics: probability of detection (0.91 and 0.85), BIAS (1.03 and 1.07), false-alarm ratio (0.12 and 0.20) and CSI (0.81 and 0.69), respectively. The 6-year test analysis showed that the model has increasing performance for high-impact events, and this performance decreases gradually as the events become weaker and more frequent. The models presented here could be useful tools for air traffic management purposes.