WRF/UCM simulations of urban heat island in guangzhou with an extracted land-use map from the remote sensing data

In order to investigate the performance of weather research and forecasting (WRF) model coupled with an urban canopy model (UCM) with an extracted land use data from Remote Sensing data (RS), three numerical experiments with different geographic models were carried out. Supervised classification with the maximum likelihood was applied to extract the land-use map in Guangzhou 2012 named the geographic model RS_12. Then based on the satellite-measured night time light data and the normalized difference vegetation index, a human settlement index was used to classify the urban land category to three urban land subcategories in the UCM as another geographic model named UCM_12. Both new geographic model simulation results are capable of reasonably modeling the observation result. The UCM_12 reasonably reproduced the best 2-m temperature evolution and the minimum root-mean- - square-error as compared with other experiments. Experiments with new geographic models can capture the strongest UHI time occured at night and gradually decreases in the morning but failure to get negative at noon. A better accuracy when comparing the new geographic models to the observation proved that extract land use data from RS can be used in urban scale simulation under the fast urbanizition in China.