This paper simulated a heat wave event which occurred around Guangzhou during late July and early August, 2012 by a weather research and forecasting (WRF) model coupled with an urban canopy model (UCM) at a horizontal resolution of 1 km. Three numerical simulations with new land-use data representing different urbanization scenarios and an default simulation with Modis land-use data were performed. The land-use data of 2012 was extracted from the Remote sensing(RS) data of year 2012 produced by the Landsat-7 satellite,then based on satellite-measured night-time light data and the normalized difference vegetation index(NDVI), a human settlement index was used to represent the current urban land-cover and define three urban land subcategories in the UCM. Using up-to-date urban land use data,which obtained as described above, simulation results agreed well with observation. The coupled WRF/UCM model reasonably reproduced the best 2-m temperature evolution and the smallest minimum mean-root-square-error as compared other experiments. The experiments coupled WRF/UCM could capturing the temporal characteristics of UHI intensity more accurately.The UHI intensity is gradually increasing after midday and becomes strongest at night, while it graduallydecreases in the morning and even gets negative at noon.The result showed that UHI intensity peak reached a maximum value of 3.0 °C at 1900 LST around sunset. Research indicates that the land-use change have a great impact on the simulation result.Comparisons among the results of four sensitivity runs showed that classification of three urban land subcategories in the experiments coupled UCM contributed 0.58℃ to lift the maximum UHI intensities,and the maximum 1.58℃ to UHI intensities. Anthropogenic heat release respectively contributed maximum 0.89 °C to the simulated UHI effects.

Delhi, the capital city of India has witnessed immense urban growth in past few decades. Many agricultural and green areas have transformed into built-up areas. The default 24 category USGS land use data used in Weather Research and Forecasting (WRF) model for mapping land use to model domain is inadequate in terms of current land use representation. Consequently, there is a substantial mismatch between land use data that is inputted to the model and actual land use especially for Delhi region. An updated land use data, thus, provides a scope in improvement of model performance. Present study is aimed at analyzing impact of change in input land cover on model outputs of some surface meteorological parameters. Three different types of land use data have been applied to the model viz. USGS land use data, MODIS based land use data and user-modified USGS land use data. Model performance has been evaluated for surface meteorological parameters like temperature, wind speed and direction and relative humidity using statistical measures. Spatial urban heat island intensities (UHI) have also been analyzed with respect to those observed in a field campaign conducted earlier. The study highlights the significance of impact of land use/land cover in atmospheric processes and the need for updated LULC for meteorological modeling.

Taiwan, especially Taipei (located in northern Taiwan), is experiencing a significant urban heat island effect due to its high population density and the uniqueness of the geographic structure. In order to evaluate the impacts of urbanization and UHI effect over northern Taiwan, a mesoscale model, Weather Research and Forecasting (WRF) model coupled with the Noah land surface model and a 2-D Urban canopy model (UCM2D), was used in this study.

The 2-D Urban canopy model was modified from original 1-D UCM (UCM1D) which already coupled in WRF model. The original UCM1D, the urban fraction (=0.7) and anthropogenic heat (50 watt/m2, in this study) are fixed in the simulation domain. We found the original UCM1D model has a tendency to underestimate air temperature in the city center during daytime while overestimated over the rural small town in northern Taiwan. In this study, we generated a new 2-D urban fraction in the simulation domain from a very high resolution of observed urban fraction (100 m resolution). Moreover, a new 2-D anthropogenic map is built from a 100 m resolution of building density in Taipei. By using this new WRF/UCM2D coupled model, it has significantly improved our simulation results for the diurnal variation during heat waves in Taipei.

In order to improve high-impact weather forecast for urban areas, a fine-scale numerical weather prediction system for urban areas (BJ-RUC-Urban), based on Weather Research and Forecasting (WRF) model, is developed with horizontal grid spacing of 1 km. BJ-RUC-Urban is nested into Beijing Rapid Update Cycle assimilation and forecasting system (BJ-RUC) with horizontal grid spacing of 9 km and 3 km. The main characteristics of BJ-RUC-Urban includes: 1) high-resolution urban land-surface dataset derived from Landsat-TM data; 2) enhanced modeling of latent heat flux from urban surfaces, e.g. irrigation and oasis effect for urban green areas; 3) using the combination of fractal dimension and fractional impervious surface to better characterize the heterogeneity of the urban morphological properties; 4) considering the release of sensible and latent heat from air conditioning system in the Build Energy Model (BEM) according to the ratio of various air conditioning types; 5) partial cycle of land surface variables; and 6) the outputs from Variational Doppler Radar Assimilation System (VDRAS) are used with the aid of four-dimensional data assimilation (FDDA). BJ-RUC-Urban is evaluated with automatic weather station (AWS) observations and quantitative precipitation estimation (QPE). Preliminary results show that BJ-RUC-Urban could well simulate urban heat island and spatial distribution of summer rainfall.