@Article{DolifNobr:2012:ArSyPa,
author = "Dolif, Giovanni and Nobre, Carlos Afonso",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Improving extreme precipitation forecasts in Rio de Janeiro,
Brazil: are synoptic patterns efficient for distinguishing
ordinary from heavy rainfall episodes?",
journal = "Atmospheric Science Letters",
year = "2012",
volume = "*",
month = "may",
keywords = "heavy rainfall forecast, Rio de Janeiro, artificial neural
network, adaptive resonance theory.",
abstract = "This work analysed heavy rainfall events and their predictability
on Rio de Janeiro, Brazil, using rain gauge data from 2000 to
2010, atmospheric model outputs, and an artificial neural network
based on adaptive resonance theory. The latter was applied on top
of atmospheric simulations for 2009 and 2010, and we were able to
predict 55% of the heavy rainfall events using a combination of
relative humidity at 900 hPa and meridional winds at 10 m for a
domain covering central and southern Brazil, which represents a
relative gain of 67% on predictability when compared to the model
predicted rainfall. Copyright © 2012 Royal Meteorological
Society.",
doi = "10.1002/asl.385",
url = "http://dx.doi.org/10.1002/asl.385",
issn = "1530-261X",
language = "en",
urlaccessdate = "04 jun. 2024"
}