@InProceedings{GarciaSantCastMont:2014:ApArNe,
author = "Garcia, Jos{\'e} Roberto M. and Santos, Rafael Duarte Coelho dos
and Castro, Christopher Cunningham and Monteiro, Antonio Miguel
Vieira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Applying artificial neural networks to calibrate the precipitation
forecast of the CPTEC’s ensemble prediction system",
booktitle = "Resumos...",
year = "2014",
editor = "Santiago J{\'u}nior, Valdivino Alexandre de and Ferreira, Karine
Reis",
organization = "Workshop dos Cursos de Computa{\c{c}}{\~a}o Aplicada do INPE,
14. (WORCAP).",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
keywords = "machine learning, artificial neural network, feed-forward neural
network, back-propagation algorithm, numerical weather prediction
system, ensemble prediction system.",
abstract = "Ensemble prediction is currently the state of the art in weather
prediction due to the fact that it provides means for computing
probabilities of the occurrence of meteorological events in a
quantitatively way. However it is not a fail-safe system and one
major cause is due to the uncertainties of the Nature that are not
modeled into the computational system, generating a deviation of
the prediction from the actual state of the weather. To minimize
this deviation (to calibrate) several post-processing techniques
over the prediction data have been applied. This work is about
applying of a feed-forward neural network to calibrate the
precipitation forecast produced by the CPTECs Ensemble Prediction
System. The dataset is composed by forecasts of the rainy season
from 2009 to 2011 over the La Plata Basin. The ensemble mean
precipitation forecast and the neural network forecast are
compared to the correspondent precipitation observations via Mean
Absolute Error.",
conference-location = "S{\~a}o Jos{\'e} dos Campos",
conference-year = "12-13 nov. 2014",
language = "en",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP8W/3HBR2PP",
url = "http://urlib.net/ibi/8JMKD3MGP8W/3HBR2PP",
targetfile = "worcap2014_submission_30.pdf",
urlaccessdate = "07 maio 2024"
}