A microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model＇s performance, using CEDVAL （Compilation of Experimental Data for Validation of Microscale Disper- sion Models） wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios; and the assessment of microscale air quality in urban areas.
The seasonal variability in the surface energy exchange of an alpine grassland on the eastern Qinghai- Tibetan Plateau was investigated using eddy covariance measurements. Based on the change of air temperature and the seasonal distribution of precipitation, a winter season and wet season were identified, which were separated by transitional periods. The annual mean net radiation （Rn） was about 39 % of the annual mean solar radiation （Rs）. Rn was relatively low during the winter season （21% of Rs） compared with the wet season （54 % of Rs）, which can be explained by the difference in surface albedo and moisture condition between the two seasons. Annually, the main consumer of net radiation was latent heat flux （LE）. During the winter season, sensible heat flux （H） was dominant because of the frozen soil condition and lack of precipita- tion. During the wet season, LE expended 66 % of Rn due to relatively high temperature and sufficient rainfall cou- pled with vegetation growth. Leaf area index （LAI） had important influence on energy partitioning during wet season. The high LAI due to high soil water content （θv） contributed to high surface conductance （go） and LE, and thus low Bowen ratio （β）. LE was strongly controlled by Rn from June to August when gc and θv were high. During the transitional periods, H and LE were nearly equally parti- tioned in the energy balance. The results also suggested that the freeze-thaw condition of soil and the seasonal distribution of precipitation had important impacts on the energy exchange in this alpine grassland.
In this study,a new parameter optimization method was used to investigate the expansion of conditional nonlinear optimal perturbation （CNOP） in a land surface model （LSM） using long-term enhanced field observations at Tongyu station in Jilin Province,China,combined with a sophisticated LSM （common land model,CoLM）.Tongyu station is a reference site of the international Coordinated Energy and Water Cycle Observations Project （CEOP） that has studied semiarid regions that have undergone desertification,salination,and degradation since late 1960s.In this study,three key land-surface parameters,namely,soil color,proportion of sand or clay in soil,and leaf-area index were chosen as parameters to be optimized.Our study comprised three experiments：First,a single-parameter optimization was performed,while the second and third experiments performed triple-and six-parameter optimizations,respectively.Notable improvements in simulating sensible heat flux （SH）,latent heat flux （LH）,soil temperature （TS）,and moisture （MS） at shallow layers were achieved using the optimized parameters.The multiple-parameter optimization experiments performed better than the single-parameter experminent.All results demonstrate that the CNOP method can be used to optimize expanded parameters in an LSM.Moreover,clear mathematical meaning,simple design structure,and rapid computability give this method great potential for further application to parameter optimization in LSMs.
A strong urban heat island （UHI） appeared in a hot weather episode in Suzhou City during the period from 25 July to 1 August 2007. This paper analyzes the urban heat island characteristics of Suzhou City under this hot weather episode. Both meteorological station observations and MODIS satellite observations show a strong urban heat island in this area. The maximum UHI intensity in this hot weather episode is 2.2℃, which is much greater than the summer average of 1.0℃ in this year and the 37–year （from 1970 to 2006） average of 0.35℃. The Weather Research and Forecasting （WRF） model simulation results demonstrate that the rapid urbanization processes in this area will enhance the UHI in intensity, horizontal distribution, and vertical extension. The UHI spatial distribution expands as the urban size increases. The vertical extension of UHI in the afternoon increases about 50 m higher under the year 2006 urban land cover than that under the 1986 urban land cover. The conversion from rural land use to urban land type also strengthens the local lake-land breeze circulations in this area and modifies the vertical wind speed field.
