@Article{BrahmVilaMartOsgo:2019:CaDiEv,
author = "Brahm, Manuel and Vila, Daniel and Martinez Saenz, Sofia and
Osgood, Daniel",
affiliation = "{The Earth Institute at Columbia University} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {The Earth Institute
at Columbia University} and {The Earth Institute at Columbia
University}",
title = "Can disaster events reporting be used to drive remote sensing
applications? A Latin America weather index insurance case study",
journal = "Meteorological Applications",
year = "2019",
volume = "26",
number = "4",
pages = "632--641",
month = "Oct.",
keywords = "disaster events, on the ground reporting, remote sensing
applications, satellite rainfall, weather index insurance.",
abstract = "A new data set was commissioned over Latin America with the goal
of supporting decision-making in various socioeconomic activities
and, in particular, for climate insurance products. The Historical
Database for Gridded Daily Precipitation Dataset over Latin
America (LatAmPrec), based on the combined scheme approach
developed at the Centro de Previs{\~a}o de Tempo e Estudos
Clim{\'a}ticos, Instituto Nacional de Pesquisas Espaciais
(CPTEC/INPE), provides a new high-resolution, low-latency,
gaugesatellite-based analysis of daily precipitation over Latin
America for the period March 2000July 2017. In order to understand
the strengths and limitations of the new data set for use in
weather index insurance, the present study applies two different
validation methodologies. The first focuses on capturing, through
a cross-correlation process, the accuracy and improved
characteristics of the new gauge-merged data set. Second, to gauge
the skill of the data set in the context of insurance losses, the
study uses a statistical approach, previously applied at a village
level and here applied at regional levels, to assess how well the
new data set predicts evidence of loss events. This is performed
for both farmer interview data and national-level disaster data
sets. The results from both validation methodologies show that
LatAmPrec performs well when compared with other data sources and
can satisfactorily capture the insurance-relevant losses on the
ground. One main advantage of the new product is its high spatial
resolution and low latency compared with other existing products
used in the weather index insurance industry.",
doi = "10.1002/met.1790",
url = "http://dx.doi.org/10.1002/met.1790",
issn = "1350-4827",
language = "en",
targetfile = "brahm_can.pdf",
urlaccessdate = "18 abr. 2024"
}