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We investigated the characteristics of surface wind speeds and temperatures predicted by the local data assimilation and prediction system (LDAPS) operated by the Korean Meteorological Administration. First, we classified automated weather stations (AWSs) into four categories (urban flat (Uf), rural flat (Rf), rural mountainous (Rm), and rural coastal (Rc) terrains) based on the surrounding land cover and topography, and selected 25 AWSs representing each category. Then we calculated the mean bias error of wind speed (WE) and temperature (TE) using AWS observations and LDAPS predictions for the 25 AWSs in each category for a period of 1 year (January–December 2015). We found that LDAPS overestimated wind speed (average WE = 1.26 m s−1) and underestimated temperature (average TE = −0.63 °C) at Uf AWSs located on flat terrain in urban areas because it failed to reflect the drag and local heating caused by buildings. At Rf, located on flat terrain in rural areas, LDAPS showed the best performance in predicting surface wind speed and temperature (average WE = 0.42 m s−1, average TE = 0.12 °C). In mountainous rural terrain (Rm), WE and TE were strongly correlated with differences between LDAPS and actual altitude. LDAPS underestimated (overestimated) wind speed (temperature) for LDAPS altitudes that were lower than actual altitude, and vice versa. In rural coastal terrain (Rc), LDAPS temperature predictions depended on whether the grid was on land or sea, whereas wind speed did not depend on grid location. LDAPS underestimated temperature at grid points on the sea, with smaller TE obtained for grid points on sea than on land.

This study aimed to evaluate the wind environment in step-up and step-down urban canyons through a computational numerical experiment using the computational fluid dynamics (CFD) model. Spatial structural conditions were considered according to the location of high-rise buildings, and the changing wind patterns inside canyons were compared and analyzed by varying the building heights. Under the step-up to step-down condition, wind velocity inside the canyon weakened, a vertical vortex formed, and vertical air flow separated; additionally, in shallow and deep canyons, wind velocity and detailed flow differed slightly according to each additional condition. For the step-down to step-up condition, the building located in the center appeared to be isolated, and a general wind environment phenomenon consistent with the step-up and step-down structures was observed. However, depending on the isolated area, an additional roof-top canyon was formed, and the wind field in the canyon was found to affect the wind velocity and detailed flow in other canyons. The wind velocity components of the inflow and outflow winds into the canyon differed based on the step-up to step-down or step-down to step-up conditions, and according to the conditions in the first and second canyons. Furthermore, the vertical wind velocity components were greatly affected by the step-up and step-down structures. Accordingly, the height and structural location of the building could affect various phenomena, such as the separation of vortices and air currents inside the canyon, and a variable wind environment was formed according to a series of conditions for the building.

We investigated the effects of wall- and tree-type fences on the airflow and fine particular matter (PM2.5) concentration around a school using a computational fluid dynamics (CFD) model. First, we validated the simulated wind speeds and PM2.5 concentrations against measured values, and the results satisfied the recommended criteria of the statistical validation indices used. Then, we evaluated the fence effects for 16 inflow directions by conducting numerical simulations with different fence types and heights. With east–southeasterly inflow, relatively high PM2.5 from the road was transported to the school. However, the wall-type fence prevented the PM2.5 from the road from entering the school, and the PM2.5 concentration decreased significantly downwind of the fence. With east–northeasterly inflow, the horizontal wind speed decreased due to the drag caused by the tree-type fence, resulting in a shift in the flow convergence region. The PM2.5 concentration decreased in the region of strengthened upward flow. This occurred because the number of pollutants transported from the background decreased. A comparison of the two fence types revealed that the effect of the tree-type fence on inbound pollutants was more significant, due to increased upward flows, than the effect of the wall-type fence.