Suzanna Maria Bonnet

This study develops an objective tool for predicting convective storms (CS) in the Terminal Maneuvering Area of Manaus (TMA-Manaus), Brazil, using machine-learning (ML) algorithms applied to thermodynamic indices derived from the 12 UTC Manaus radiosonde. Atmospheric discharges (AD) are used as both a proxy and a severity metric for CS activity, and five AD-based thresholds are defined to characterize increasing levels of convective intensity. A 4-h forecast horizon was adopted, consistent with the interval between the pre-convective sounding and the afternoon lightning peak over the region. Feature-selection analysis identified a physically meaningful subset of 16 sounding-derived predictors, with Precipitable Water, Lifted Index, CAPE, and the Bulk Richardson Number among the most relevant variables for CS prediction. At the lowest threshold (DR-AD > 75.5 day ^-1 ), the Quadratic Discriminant Analysis (QDA) model achieved the best overall result with the 24-feature configuration, reaching a Probability of Detection (POD) of 0.99 and a False Alarm Ratio (FAR) of 0.20. Models based on reduced predictor sets also retained competitive skill, confirming that dimensionality reduction can preserve the essential physical information required for operational forecasting. A permutation-importance analysis further supported the physical consistency of the selected predictors and provided model-agnostic interpretability. Although predictive performance gradually decreased as the AD threshold increased, useful skill was retained even under more imbalanced and severe-event conditions. This represents the first radar-free, AD-based convective forecast framework for the Amazon region that satisfies Brazil’s aeronautical nowcasting lead-time standard (ICA 100–12). The proposed method is suitable for real-time implementation wherever a 12 UTC sounding and automated feature computation are available.

Precipitation nowcasting can predict and alert for any possibility of abrupt weather changes which may cause both human and material risks. Most of the conventional nowcasting methods extrapolate weather radar echoes, but precipitation nowcasting is still a challenge, mainly due to rapid changes in meteorological systems and time required for numerical simulations. Recently video prediction deep learning (VPDL) algorithms have been applied in precipitation nowcasting. In this study, we use the VPDL PredRNN++ and sequences of radar reflectivity images to predict the future sequence of reflectivity images for up to 1-h lead time for São Paulo, Brazil. We also verify the feasibility for the continuous use of the VPDL model, providing the meteorologist with trends and forecasts in precipitation edges regardless of the weather event occurring. The results obtained confirm the potential of the VPDL model as an additional tool to assist nowcasting. Even though meteorological systems that trigger natural disasters vary by location, a general solution can contribute as a tool to assist decision-makers and consequently issue efficient alerts.

Resumo No Município do Rio de Janeiro (MRJ), verifica-se em certas situações de deslocamento de Sistemas Frontais (SFs), a ocorrência de chuvas fracas a moderadas, mesmo após a completa passagem do SF pelo Estado. Este fenômeno é conhecido na região como precipitação devido ao “efeito de circulação marítima”, mas não apresenta documentação, ficando restrito ao conhecimento dos previsores locais. O objetivo deste trabalho é elaborar uma climatologia da frequência de ocorrência desse fenômeno no MRJ para o período 2000-2013 e identificar as condições sinóticas associadas. A partir de imagens de satélite e dados de precipitação, as passagens de SFs pelo MRJ foram classificadas em três tipos: Pós-Frontais Sem Chuva (PFSC); Pós-Frontais Com Chuva (PFCC) e Pós-Frontais Excluídos (PFE). Os resultados indicam que mais da metade dos eventos são do tipo PFE (52%), 32% do tipo PFSC e 16% do tipo PFCC. Os casos PFE predominam no verão, os PFSC no inverno e primavera e os PFCC no outono. Os compostos dos casos PFSC e PFCC utilizando a Reanálise ERA-Interim indicam que a precipitação pós-frontal estaria associada à convergência de fortes ventos de sul junto à costa ao penetrar no continente, formando nuvens estratiformes, apesar do SF não estar mais atuando.