Based on the NOAA’s Advanced Very High Resolution Radiometer （AVHRR） Pathfi nder Atmospheres Extended （PATMOS-x） monthly mean cloud amount data, variations of annual and seasonal mean cloud amount over the Yangtze River Delta （YRD）, China were examined for the period 1982-2006 by using a linear regression analysis. Both total and high-level cloud amounts peak in June and reach minimum in December, mid-level clouds have a peak during winter months and reach a minimum in summer, and low-level clouds vary weakly throughout the year with a weak maximum from August to October. For the annual mean cloud amount, a slightly decreasing tendency （-0.6% sky cover per decade） of total cloud amount is observed during the studying period, which is mainly due to the reduction of annual mean high-level cloud amount （-2.2% sky cover per decade）. Mid-level clouds occur least （approximately 15% sky cover） and remain invariant, while the low-level cloud amount shows a signifi cant increase during spring （1.5% sky cover per decade） and summer （3.0%sky cover per decade）. Further analysis has revealed that the increased low-level clouds during the summer season are mainly impacted by the local environment. For example, compared to the low-level cloud amounts over the adjacent rural areas （e.g., cropland, large water body, and mountain areas covered by forest）, those over and around urban agglomerations rise more dramatically.
The simulation performance over complex building clusters of a wind simulation model（Wind Information Field Fast Analysis model, WIFFA） in a micro-scale air pollutant dispersion model system（Urban Microscale Air Pollution dispersion Simulation model, UMAPS） is evaluated using various wind tunnel experimental data including the CEDVAL（Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models） wind tunnel experiment data and the NJU-FZ experiment data（Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data）. The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier–Stokes equations, and can produce very high-resolution（1–5 m） wind fields of a complex neighborhood scale urban building canopy（～ 1 km ×1km） in less than 3 min when run on a personal computer.
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.
