Data assimilation is important for the spatial analysis of small regions with complex terrain and diverse climates and for interpolation among observations. A data assimilation method incorporating observations, coarse-grid re-analysis data and physiographical features is demonstrated to generate high-resolution temperature data for small islands such as Taiwan. The method is also able to weigh physiographic and anthropogenic factors. Among the spatial factors, the orographic effect is the dominating factor and the lapse rate varies seasonally. Population density is significantly related to temperature, which may correspond to the urban heat-island (UHI) effect. It is also shown that an anthropogenic factor could be used with this interpolation method to explain the details of the temperature variation. The data assimilation model provides an opportunity to assess the extent to which simple statistical regression equations, calibrated from natural variability, can reproduce climate changes driven by land effects without considering a complex climate model. Copyright © 2012 Royal Meteorological Society
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