@Article{CaseriSantStep:2022:CoReNe,
author = "Caseri, Ang{\'e}lica N. and Santos, Leonardo Bacelar Lima and
Stephany, Stephan",
affiliation = "{Centro Nacional de Monitoramento e Alerta de Desastres Naturais
(CEMADEN)} and {Centro Nacional de Monitoramento e Alerta de
Desastres Naturais (CEMADEN)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)}",
title = "A convolutional recurrent neural network for strong convective
rainfall nowcasting using weather radar data in Southeastern
Brazil",
journal = "Artificial Intelligence in Geosciences",
year = "2022",
volume = "3",
pages = "8--13",
month = "Dec.",
keywords = "Deep learning, Extreme events, Nowcasting, Rainfall, Weather
radar.",
abstract = "Strong convective systems and the associated heavy rainfall events
can trig-ger floods and landslides with severe detrimental
consequences. These events have a high spatio-temporal
variability, being difficult to predict by standard meteorological
numerical models. This work proposes the M5Images method for
performing the very short-term prediction (nowcasting) of heavy
convective rainfall using weather radar data by means of a
convolutional recurrent neural network. The recurrent part of it
is a Long Short-Term Memory (LSTM) neural network. Prediction
tests were performed for the city and surroundings of Campinas,
located in the Southeastern Brazil. The convolutional recurrent
neural network was trained using time series of rainfall rate
images derived from weather radar data for a selected set of heavy
rainfall events. The attained pre-diction performance was better
than that given by the persistence forecasting method for
different prediction times.",
doi = "10.1016/j.aiig.2022.06.001",
url = "http://dx.doi.org/10.1016/j.aiig.2022.06.001",
issn = "2666-5441",
language = "en",
targetfile = "A convolutional recurrent neural network for strong convective
rainfall nowcasting using weather radar data in Southeastern
Brazil.pdf",
urlaccessdate = "21 maio 2024"
}