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Constructing climate layers is more difficult and important in mountainous areas as a result of sparse meteorological stations and complex topography. This requires a 2-stage process: quality control of meteorological data and spatial interpolation of climate data. For this article, unscreened metadata and observed data were collected from all stations in Taiwan for the period 1961-2002. A quality-control procedure based on a geographic information system (GIS) allowed us to reject 13.5% of stations because of missing or erroneous metadata and filter out 8.3% of the observed data because of extreme errors or unreasonable temporal sequence and spatial patterns. After applying the quality-control procedure, the monthly mean temperature and total monthly precipitation were calculated as spatial interpolation sampling points. We evaluated the performance of 6 kriging-based spatial interpolation methods with regard to their errors by cross-validation. For interpolating the monthly mean temperature, the strong relation between temperature and elevation led us to favor modified residual kriging. For interpolating the total monthly precipitation, log-transformed kriging was chosen for practical reasons (steadier and simpler). We compared our product layers with pre-existing climate layers. The overall spatial patterns of these layers were similar, except for certain extremes in the mountains. Consequently, the GIS-based approaches presented here could help in rapid construction of adequate climate layers for regions with unconfirmed data

GIS-based Tests for Quality Control of Meteorological Data and Spatial Interpolation of Climate Data