@Article{GonçalvesCosAngRodSou:2012:ReGORe,
author = "Gon{\c{c}}alves, Weber Andrade and Costa, Simone Marilene Sievert
da and Angelis, Carlos Frederico de and Rodrigues, Jurandir
Ventura and Souza, Rodrigo Augusto Ferreira",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de
Pesquisas Espaciais (INPE)} and {Instituto Nacional de Pesquisas
Espaciais (INPE)} and {Universidade Estadual do Amazonas}",
title = "Regionalization of the GOES-10 retrieval algorithm for tropical
South America",
journal = "International Journal of Remote Sensing",
year = "2012",
volume = "33",
number = "17",
pages = "5366 - 5378",
keywords = "sounder, profiles.",
abstract = "Meteorological satellites provide a unique opportunity to obtain
thermodynamic profiles in regions of the globe that do not have a
dense meteorological upper air stations network, as in South
America. The geostationary satellite GOES-10 made the inference of
temperature and mixing ratio profiles every hour with a special
resolution of 10 km over South America from July 2007 to February
2009. The GOES-10 retrieval algorithm for thermodynamic profiles
was developed by the CIMMS in the United States, so some
adjustments for its application in South America could be done.
Among these adjustments is the construction of a new covariance
matrix. In this context, the scientific focus of this research was
to construct a new covariance matrix adapted to meteorological
conditions of South America. In addition, a validation of the
algorithm results by the use of the original and the new
covariance matrices was performed. The variables validated were
the air temperature and mixing ratio vertical profiles and the
values of total precipitable water. The dataset used was a total
of 1095 radiosonde observations located in South America tropical
region at 00:00 and 12:00 UTC, as well as thermodynamic profiles
from 12h forecasts of the CPTEC Global Model, used as first guess,
and upwelling radiances of 18 infrared channels from GOES-10
satellite for the period from July to November 2007. In general,
the results indicated that with the regionalization of the
covariance matrix the algorithm performed better retrievals than
when it used the original matrix. The greatest improvements were
found in the mixing ratio profiles and in the values of total
precipitable water. These results could be associated to the
presence of the Amazon Rainforest that incorporated a greater
amount of moisture in the new covariance matrix than the previous
matrix had.",
doi = "10.1080/01431161.2012.657367",
url = "http://dx.doi.org/10.1080/01431161.2012.657367",
issn = "0143-1161",
language = "en",
urlaccessdate = "11 maio 2024"
}