@InProceedings{LimaPezzPenn:2017:InInAs,
author = "Lima, Leonardo Nascimento and Pezzi, Luciano Ponzi and Penny,
Stephen Gregory",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Uma investiga{\c{c}}{\~a}o das incertezas associadas {\`a}
modelagem num{\'e}rica dos oceanos atrav{\'e}s de experimentos
de previs{\~a}o e assimila{\c{c}}{\~a}o de dados por conjuntos
no Atl{\^a}ntico Sudoeste",
booktitle = "Anais...",
year = "2017",
editor = "Gherardi, Douglas Francisco Marcolino and Arag{\~a}o, Luiz
Eduardo Oliveira e Cruz de",
pages = "6233--6240",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 18. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "Numerical ocean models incorporate errors originating from
different sources (e.g. atmospheric forcing, physics
parameterizations, boundary conditions, bathymetry, numerical
error). Data assimilation provides an important tool for
correcting the numerical representation generated by the ocean
model itself. In this study, ensemble experiments were performed
by using the Regional Ocean Modeling System (ROMS) in the
Southwest Atlantic Ocean (55ºS 5ºS; 70ºW 20ºW), with the aim to
investigate uncertainties in the ocean state that derived from
perturbations in atmospheric forcing and ocean bathymetry.
Ensemble experiments that incorporated different atmospheric
perturbations exhibited the main qualitative differences between
the members during the first months of integration. The wind
component perturbations dominated and provoked the greatest impact
in the ocean ensemble spread as compared with other atmospheric
variables. Even though as a terrain-following vertical coordinate
model, ROMS proved to be more sensitive to perturbations in
bathymetry, particularly in shallow waters. Next, the Local
Ensemble Transform Kalman Filter (LETKF) was applied to ROMS to
examine the impact of observed temperature and salinity (TS)
profiles on a regional ocean analysis. The assimilation of TS
profiles improved the thermohaline representation. For example,
the area-averaged root mean square deviation of temperature was
2.30ºC for the free model run and was reduced to 0.95°C for the
LETKF analyses. The next step will be to assimilate the Sea
Surface Temperature (SST) and Absolute Dynamic Topography (ADT)
observation data to provide further constraints on the ocean
mesoscale in the study region.",
conference-location = "Santos",
conference-year = "28-31 maio 2017",
isbn = "978-85-17-00088-1",
label = "59347",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "8JMKD3MGP6W34M/3PSMCGU",
url = "http://urlib.net/ibi/8JMKD3MGP6W34M/3PSMCGU",
targetfile = "59347.pdf",
type = "Oceanografia",
urlaccessdate = "27 abr. 2024"
}