@Article{OliveiraMaggVilaPorc:2018:UsSaEr,
author = "Oliveira, R{\^o}mulo Augusto Juc{\'a} and Maggioni, Viviana and
Vila, Daniel Alejandro and Porcacchia, Leonardo",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {George
Mason University} and {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {George Mason University}",
title = "Using satellite error modeling to improve GPM-Level 3 rainfall
estimates over the central Amazon region",
journal = "Remote Sensing",
year = "2018",
volume = "10",
number = "2",
pages = "e336",
month = "Feb.",
keywords = "global precipitation measurement, IMERG, PUSH, error model,
validation, Amazon.",
abstract = ": This study aims to assess the characteristics and uncertainty of
Integrated Multisatellite Retrievals for Global Precipitation
Measurement (GPM) (IMERG) Level 3 rainfall estimates and to
improve those estimates using an error model over the central
Amazon region. The S-band Amazon Protection National System
(SIPAM) radar is used as reference and the Precipitation
Uncertainties for Satellite Hydrology (PUSH) framework is adopted
to characterize uncertainties associated with the satellite
precipitation product. PUSH is calibrated and validated for the
study region and takes into account factors like seasonality and
surface type (i.e., land and river). Results demonstrated that the
PUSH model is suitable for characterizing errors in the IMERG
algorithm when compared with S-band SIPAM radar estimates. PUSH
could efficiently predict the satellite rainfall error
distribution in terms of spatial and intensity distribution.
However, an underestimation (overestimation) of light satellite
rain rates was observed during the dry (wet) period, mainly over
rivers. Although the estimated error showed a lower standard
deviation than the observed error, the correlation between
satellite and radar rainfall was high and the systematic error was
well captured along the Negro, Solim{\~o}es, and Amazon rivers,
especially during the wet season.",
doi = "10.3390/rs10020336",
url = "http://dx.doi.org/10.3390/rs10020336",
issn = "2072-4292",
language = "en",
urlaccessdate = "25 abr. 2024"
}