@InCollection{StephanyStCaLiGaPe:2019:DaMiAp,
author = "Stephany, Stephan and Strauss, Cesar and Calheiros, Alan James
Peixoto and Lima, Glauston Roberto Teixeira de and Garcia,
Jo{\~a}o Victor Cal and Pessoa, Alex Sandro Aguiar",
title = "Data mining approaches to the real-time monitoring and early
warning of convective weather using lightning data",
booktitle = "Towards mathematics, computers and environment: a disasters
perspective",
publisher = "Springer Nature",
year = "2019",
editor = "Santos, Leonardo Bacelar Lima and Negri, Rog{\'e}rio Galante and
Carvalho, Tiago Jos{\'e} de",
pages = "83--101",
address = "Cham, Switzerland",
keywords = "data mining, real time monitoring, convective weather.",
abstract = "Tracking and monitoring of convective events may require the
analysis of a huge amount of data from sensors on the ground, such
as weather radars, or on board of satellites, in addition to the
forecasts of numerical models. Thunderstorms associated with
severe convective events have a potential to cause strong winds,
floods, and landslides, with serious environmental and
socio-economic impacts. New approaches based on data mining have
been proposed for countries like Brazil that lack a complete
weather radar coverage, but have some ground-based lightning
detector networks. Lightning data may help to visualize the
current state of convective systems in near real-time or to
estimate the amount of convective precipitation in a given area
and period of time. Data mining algorithms can be trained using
numerical model data and lightning data yielding specific data
mining models, which can be used to predict the occurrence of
convective activity from numerical model forecasts. These data
mining models may help meteorologists to improve the accuracy of
early warnings and forecastings, mainly in countries that lack a
complete weather radar coverage.",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Centro Nacional de Monitoramento
e Alertas de Desastres Naturais (CEMADEN)} and {Centro Nacional de
Monitoramento e Alertas de Desastres Naturais (CEMADEN)} and
{Climatempo Meteorologia}",
doi = "10.1007/978-3-030-21205-6_5",
url = "http://dx.doi.org/10.1007/978-3-030-21205-6_5",
isbn = "978-3-030-21204-9 and {978-3-030-21205-6 (eBook)}",
language = "en",
targetfile = "stephany_data.pdf",
urlaccessdate = "28 abr. 2024"
}