Scale matching of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model: a
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ORIGINAL PAPER
Scale matching of multiscale digital elevation model (DEM) data and the Weather Research and Forecasting (WRF) model: a case study of meteorological simulation in Hong Kong Chunxiao Zhang & Hui Lin & Min Chen & Liang Yang
Received: 18 July 2013 / Accepted: 3 January 2014 # Saudi Society for Geosciences 2014
Abstract It is becoming easier to combine geographical data and dynamic models to provide information for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process can confuse the results. This study extends traditional scale research in static geographical patterns to dynamic processes and focuses on the combined scale effect of multiscale geographical data and dynamic models. The capacity for topographical expression under the combined scale effect was investigated by taking multiscale topographical data and meteorological processes in Hong Kong as a case study. A meteorological simulation of the combined scale effect was evaluated against data from Hong Kong Observatory stations. The experiments showed that (1) a digital elevation model (DEM) using 3 arc sec data with a 1 km resolution Weather Research and Forecasting (WRF) model gives better topographical expression and meteorological reproduction in Hong Kong; (2) a fine-scale model is sensitive to the resolution of the DEM data, whereas a coarse-scale model is less sensitive to it; (3) better topographical expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the DEM data and the dynamic model may account C. Zhang : H. Lin (*) : M. Chen Institute of Space and Earth Information Science, Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China e-mail: [email protected] H. Lin Department of Geography and Resource Management, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China H. Lin : M. Chen Shenzhen Research Institute, The Chinese University of Hong Kong, ShenZhen 518057, China L. Yang Department of Geography, KlimaCampus, University of Hamburg, Grindelberg 5, 20144 Hamburg, Germany
for 38 % of the variance in certain meteorological variables (e.g., temperature). This case study gives a clear explanation of the significance and implementation of scale matching for multiscale geographical data and dynamic models. Keywords Scale matching . Digital elevation model (DEM) data . Weather Research and Forecasting (WRF) model . Meteorological simulation . Mean absolute error (MAE)
Introduction Geographical data and dynamic models (including statistical and numerical models) are key components in studies of geographical processes. Researchers argue that when datasets are combined in a model to solve a geographical problem, each has scale characteristics that must be considered (Lilburne et al. 2004; Wu 2004; Zhang et al. 2014). It is widely known that geographical data have multiscale characteristics that generate scale effects when such data are used in analysis (Ouadfeul and Aliouane 2013). Examples include
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