@Article{SiqueiraVila:2019:HyMePr,
author = "Siqueira, Ricardo Almeida de and Vila, Daniel Alejandro",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "Hybrid methodology for precipitation estimation using
Hydro-Estimator over Brazil",
journal = "International Journal of Remote Sensing",
year = "2019",
volume = "40",
number = "11",
pages = "4244--4263",
abstract = "Rainfall measurement is a very important topic to society and for
the understanding of the weather and climate, therefore needs to
be calculated as accurately as possible. Counteracting the problem
of the high temporal and spatial variability of precipitation,
geostationary satellites sensors have been proved an excellent
tool to this task, providing scans with high temporal resolution
and detecting the growth and decay of rain cells. Using infra-red
(IR) images obtained from the Geostationary Operational
Environmental Satellites (GOES), the Hydro-Estimator (HYDRO)
algorithm produces instantaneous precipitation estimates with 30
min temporal resolution and 4 km spatial resolution with a very
low latency compared with other more sophisticated methodologies
(i.e. passive microwave-based algorithms). However, the IR
algorithm has some limitations to estimate precipitation on some
cloud systems. In order to overcome this problem, the main
objective of this study is to develop a light and fast algorithm,
based on the histogram matching (HM) technique, to combine the
superior sampling and low latency of the HYDRO IR product with
more accurate active microwave-based products over Brazil. The
adjusted HYDRO (AHYDRO) product was validated against Brazil rain
gauge network for two years (2016-2017) and the performance was
assessed by using standard statistical metrics and categorical
indices. Results show that the HM technique is able to minimize
the large variability and discrepancies among HYDRO and observed
precipitation over Brazil. At same time, is able to generate a
better bias performance while maintaining the same correlation
levels before the adjustment.",
doi = "10.1080/01431161.2018.1562262",
url = "http://dx.doi.org/10.1080/01431161.2018.1562262",
issn = "0143-1161",
language = "en",
targetfile = "Hybrid methodology for precipitation estimation using Hydro
Estimator over Brazil.pdf",
urlaccessdate = "01 maio 2024"
}