@Article{PezziKaya:2009:AnSePr,
author = "Pezzi, Luciano Ponzi and Kayano, Mary Toshie",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "An analysis of the seasonal precipitation forecasts in South
America using wavelets",
journal = "International Journal of Climatology",
year = "2009",
volume = "29",
number = "11",
pages = "1560--1573",
month = "Sep.",
keywords = "seasonal forecast, ensemble forecast, South America precipitation,
wavelet analysis.",
abstract = "A post-processing technique was applied to statistically correct
the seasonal rainfall forecasts over South America (SA). The aim
of this work was to reduce errors in the seasonal climate
simulations obtained from the Centro de Previsao de Tempo e
Estudos Clim´aticos (CPTEC) atmospheric general circulation model
(AGCM) which was run with different deep cumulus convection
parameterizations. One of the main contributions of this study is
the discussion of the super-ensemble approach to reduce errors in
the seasonal rainfall prediction for SA. A novel aspect here is
the use of the wavelet technique to compare forecast and observed
time series by investigating their time-frequency structures. This
methodology has not yet been applied to super-ensemble model
validations. The statistical algorithm used in the superensemble
technique was based on the linear multiple regression method. The
time series of the super-ensemble forecast (FCT), arithmetic
averaged forecast (MEM) and individual model forecasts and the
observed (OBS) ones for selected areas of SA were compared by
calculating the root mean square errors (RMSEs) and by applying
the wavelet technique on these time series. In general, for the
analysed areas we obtained a super-ensemble skill superior to that
for the MEM. The wavelet analysis proved to be very useful to
compare forecast and observed time series. In fact, differences
and similarities among the time series such as the dominant scale
of variability and the time location of the largest variances in
the time series were detected with the wavelet analyses.",
doi = "10.1002/joc.1813",
url = "http://dx.doi.org/10.1002/joc.1813",
issn = "0899-8418",
language = "en",
targetfile = "an analysis.pdf",
urlaccessdate = "15 jun. 2024"
}