Spatial Downscaling of TRMM Precipitation Using Geostatistics and FineScale Environmental Variables

Collection with item attached
2013
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/485039.do
DOI
10.1155/2013/237126
Title
Spatial Downscaling of TRMM Precipitation Using Geostatistics and FineScale Environmental Variables
Description
This research was supported by the Basic Science Research Programthrough the National Research Foundation of Korea (NRF) funded by theMinistry of Science, ICT, and Future Planning (NRF-2012R1A1A1005024).
abstract
A geostatistical downscaling scheme is presented and can generate fine scale precipitation information from coarse scale Tropical Rainfall Measuring Mission (TRMM) data by incorporating auxiliary fine scale environmental variables. Within the geostatistical framework, the TRMM precipitation data are first decomposed into trend and residual components. Quantitative relationships between coarse scale TRMM data and environmental variables are then estimated via regression analysis and used to derive trend components at a fine scale. Next, the residual components, which are the differences between the trend components and the original TRMM data, are then downscaled at a target fine scale via area-to-point kriging. The trend and residual components are finally added to generate fine scale precipitation estimates. Stochastic simulation is also applied to the residual components in order to generate multiple alternative realizations and to compute uncertainty measures. From an experiment using a digital elevation model (DEM) and normalized difference vegetation index (NDVI), the geostatistical downscaling scheme generated the downscaling results that reflected detailed characteristics with better predictive performance, when compared with downscaling without the environmental variables. Multiple realizations and uncertainty measures from simulation also provided useful information for interpretations and further environmental modeling.
provenance
Made available in Cube on 2018-09-28T15:30:11Z (GMT). No. of bitstreams: 0
language
English
author
Park, No-Wook
accessioned
2018-09-28T15:30:11Z
available
2018-09-28T15:30:11Z
issued
2013
citation
ADVANCES IN METEOROLOGY
issn
1687-9309
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/485039.do
Funder
미래창조과학부
Funding Program
일반연구자지원(미래부)
Project ID
1711004405
Jurisdiction
Rep.of Korea
Project Name
Development of integrated modeling techniques of environmental spatial information and disease data for medical geoinformatics
rights
openAccess
type
article


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