1 / 2018-03-11 14:13:54

Scale matching of multiscale Land use data and the Weather Research and Forecasting (WRF) model: a case study of meteorological simulation in Beijing Region

WRF,Land use data,Beijing,Scale matching

It is becoming easier to integrate geographical data and dynamic models to conduct simulation for problem solving and geographical cognition. However, the scale dependencies of the data, model, and process may confuse the results. Extending the traditional scale research in static geographical patterns to dynamic processes, this paper focuses on the quantitative analysis of scale matching between multiscale land use data and the Weather Research and Forecasting (WRF) model. By scale effect analysis of multiscale land use data in Beijing, Tianjin and Hebei region, we select the data on characteristic scales to express the pattern of land use. And then, a meteorological simulation of the combined scale effect was evaluated against data from observations. The results show that (1) using 3 arc sec data with 1 km resolution WRF model gives better land use expression and meteorological reproduction in the study area; (2) a fine-scale model is sensitive to the resolution of the land use data, whereas a coarse-scale model is less sensitive to it; (3) better land use expression alone does not improve weather process simulation; and (4) uncertainty arising from a scale mismatch between the land use data and the dynamic model may account for 23 % 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.