@Article{ValverdeArauCamp:2014:NeNeFu,
author = "Valverde, M. C. and Araujo, E. and Campos Velho, Haroldo Fraga
de",
affiliation = "{Universidade Federal do ABC (UFABC)} and {Intelig{\^e}ncia
Artificial em Tecnologia (IATECH)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Neural network and fuzzy logic statistical downscaling of
atmospheric circulation-type specific weather pattern for rainfall
forecasting",
journal = "Applied Soft Computing",
year = "2014",
volume = "22",
pages = "681--694",
keywords = "Climatology, Clouds, Disaster prevention, Disasters, Fuzzy logic,
Neural networks, Rain, Soft computing, Weather forecasting, Daily
rainfall forecasting, Multi-linear regression, Natural disasters,
Performance comparison, Rainfall forecasting, South atlantic
convergence zones, Statistical downscaling, Time-spatial series,
Statistics.",
abstract = "The weather natural disaster prevention for quantitative daily
rainfall forecasting derived from the SACZ-ULCV weather pattern is
proposed in this paper by using intertwined statistical
downscaling (SD) and soft computing (SC) approaches. The fuzzy
statistical downscaling (FSD) is first introduced and, then,
employed for dealing with the SACZ-ULCV atmospheric
circulation-type specific weather pattern for supporting daily
precipitation (rainfall) forecasting. This paper also addresses
the performance comparison of the FSD and the neural statistical
downscaling (NSD) approaches when taking into account 12 major
urban centers all over the state of S{\~a}o Paulo, Brazil, for
the summer period. The SACZ-ULCV summer pattern is identified in
meteorological satellite images when the cloudiness of the
Brazilian Northeast upper level cyclonic vortices (ULCV) meets the
South Atlantic convergence zone (SACZ). Increasing the convection
and the cloudiness over the Southeast region of Brazil, the
SACZ-ULCV causes severe rainfalls and thunderstorms with impact on
the population. Finding a manner to anticipate these extreme
rainfall events is of vital importance for minimizing or avoiding
disasters, and saving lives. Daily rainfall forecasting had their
performance improved either by using the proposed FSD or NSD in
comparison to the Multilinear Regression ETA model. Results
demonstrate the FSD and the NSD become feasible alternatives for
achieving a correspondence from meteorological and
thermo-dynamical variables to the daily rainfall variable.",
doi = "10.1016/j.asoc.2014.02.025",
url = "http://dx.doi.org/10.1016/j.asoc.2014.02.025",
issn = "1568-4946",
label = "scopus 2014-11 ValverdeArauCamp:2014:NeNeFu",
language = "en",
urlaccessdate = "27 abr. 2024"
}