engleski

Methods and data sources for spatial prediction of rainfall

This chapter reviews both rain gauge (point) data sources and remote-sensing (visible, thermal IR and microwave (MW)) imagery sources used for producing precipitation maps, and then shows “in action” a number of mechanical and stochastic spatial prediction methods that can be used to generate maps of rainfall intensity. Special focus was put on using geostatistical techniques implemented in the R environment for statistical computing (via stats, gstat, and geoR packages). The spatial prediction methods are illustrated using a small case study (97 points obtained from the National Climatic Data Center Global Summary of Day) covering the scanning radius of the Bilogora weather radar in Croatia (366 daily images) and the national rain gauge network in Italy (1901 stations). The results show that the rainfall estimated using ground-based radar can be of variable accuracy. The radar images can contain many artifacts, especially at high distances from the ground radar, so that the correlation with ground measurements is often marginal. Daily rainfall intensity is commonly skewed toward small values with many zeros, thus rainfall intensity estimates at shorter time intervals are suitable for modeling using zero-inflated regression models. The chapter contains code snippets showing how to implement various prediction techniques from local trend surfaces to ordinary kriging, zero-inflated regression models, and regression-kriging in R.

rain gauge, remote-sensing, precipitation map, spatial prediction, trend surfaces, ordinary kriging, zero-inflated regression, regression-kriging

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