@Article{RuivoSampRamo:2014:Ap2020,
author = "Ruivo, Heloisa Musetti and Sampaio, Gilvan and Ramos, Fernando
Manuel",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Knowledge extraction from large climatological data sets using a
genome-wide analysis approach: application to the 2005 and 2010
Amazon droughts",
journal = "Climatic Change",
year = "2014",
volume = "123",
pages = "1",
abstract = "Today, the volume of data generated in almost all disciplines,
particularly in meteorology and climate science, is dramatically
increasing. Among the challenges generated by this data deluge is
the development of efficient knowledge discovery strategies. Here,
we show that statistical and computational tools used to analyze
large data sets of genome-wide studies can be fruitfully applied
to a climatic context. Although not as powerful as some techniques
already in use by climatologists, these tools are simple and
robust, and can easily be adapted to detect early warning signals
for extreme events like droughts or be used to filter large data
sets before applying other more advanced and computationally
expensive methods. We test this approach in our investigation of
the causes of the Amazon droughts of 2005 and 2010. Our results
highlight the major role played in these extreme events by the
warming of the seas surface temperature, mainly in the tropical
North Atlantic. Our findings are in agreement with several
analyses published in the literature. The main message we convey
is that free and open-source data mining and visualization
techniques routinely used in genetic studies can be useful in
helping scientists to extract knowledge from large climatic data
sets, particularly in regions of the world that are vulnerable to
climate change but where the availability of technical expertise
is critically scarce.",
doi = "10.1007/s10584-014-1066-7",
url = "http://dx.doi.org/10.1007/s10584-014-1066-7",
issn = "0165-0009",
label = "lattes: 0236607123089481 2 RuivoSampMRam:2014:Ap2020",
language = "en",
urlaccessdate = "24 abr. 2024"
}