@Article{AfonsoViGaQuBaChPa:2020:PrDiCy,
author = "Afonso, Jo{\~a}o Maria de Sousa and Vila, Daniel Alejandro and
Gan, Manoel Alonso and Quispe, David Pareja and Barreto, Naurinete
de Jesus da Costa and Chinchay, Joao Henry Huam{\'a}n and
Palharini, Rayana Santos Araujo",
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 {Instituto Nacional de Pesquisas Espaciais
(INPE)} and {Servicio Nacional de Meteorolog{\'{\i}}a e
Hidrolog{\'{\i}}a del Per{\'u}} and {Instituto Nacional de
Pesquisas Espaciais (INPE)}",
title = "Precipitation diurnal cycle assessment of satellite-based
estimates over Brazil",
journal = "Remote Sensing",
year = "2020",
volume = "12",
number = "14",
pages = "e2339",
month = "July",
keywords = "precipitation, GSMaP, IMERG, CMORPH.",
abstract = "The main objective of this study is to assess the ability of
several high-resolution satellite-based precipitation estimates to
represent the Precipitation Diurnal Cycle (PDC) over Brazil during
the 20142018 period, after the launch of the Global Precipitation
Measurement satellite (GPM). The selected algorithms are the
Global Satellite Mapping of Precipitation (GSMaP), The Integrated
Multi-satellitE Retrievals for GPM (IMERG) and Climate Prediction
Center (CPC) MORPHing technique (CMORPH). Hourly rain gauge data
from different national and regional networks were used as the
reference dataset after going through rigid quality control tests.
All datasets were interpolated to a common 0.1\◦ ×
0.1\◦ grid every 3 h for comparison. After a hierarchical
cluster analysis, seven regions with different PDC characteristics
(amplitude and phase) were selected for this study. The main
results of this research could be summarized as follow: (i) Those
regions where thermal heating produce deep convective clouds, the
PDC is better represented by all algorithms (in term of amplitude
and phase) than those regions driven by shallow convection or
low-level circulation; (ii) the GSMaP suite (GSMaP-Gauge (G) and
GSMaP-Motion Vector Kalman (MVK)), in general terms, outperforms
the rest of the algorithms with lower bias and less dispersion. In
this case, the gauge-adjusted version improves the satellite-only
retrievals of the same algorithm suggesting that daily
gauge-analysis is useful to reduce the bias in a sub-daily scale;
(iii) IMERG suite (IMERG-Late (L) and IMERG-Final (F))
overestimates rainfall for almost all times and all the regions,
while the satellite-only version provide better results than the
final version; (iv) CMORPH has the better performance for a
transitional regime between a coastal land-sea breeze and a
continental amazonian regime. Further research should be performed
to understand how shallow clouds processes and
convective/stratiform classification is performed in each
algorithm to improve the representativity of diurnal cycle.",
doi = "10.3390/rs12142339",
url = "http://dx.doi.org/10.3390/rs12142339",
issn = "2072-4292",
language = "en",
targetfile = "remotesensing-12-02339.pdf",
urlaccessdate = "24 abr. 2024"
}