%0 Journal Article
%T Spatial Downscaling of TRMM Precipitation Using Geostatistics and Fine Scale Environmental Variables
%A No-Wook Park
%J Advances in Meteorology
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/237126
%X 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. 1. Introduction Precipitation information has been regarded as one of the important information sources for understanding hydrological, ecological, and environmental systems [1¨C3]. This information can be obtained from either rain gauge station data or remote sensing data. Although precise precipitation information can be obtained from rain gauge data, few rain gauge data are usually available, which may hinder the generation of reliable maps. Remote sensing data that can provide periodic and exhaustive information can be effectively used to map precipitation information. For example, the Global Precipitation Climatology Project (GPCP) [4] and the Tropical Rainfall Measuring Mission (TRMM) [5] provide precipitation data at regional and global scales [3]. As an international satellite mission, the Global Precipitation Measurement (GPM) mission, scheduled to launch in 2014, will also provide next-generation observations of rain and snow worldwide [6]. Although these missions or projects can provide time-series precipitation information, their spatial resolutions are too coarse to be applied to local analysis (e.g., the finest
%U http://www.hindawi.com/journals/amete/2013/237126/