The urban climate has a critical influence on developing sustainable cities, and one important factor is the urban wind environment. Moreover, refining urban wind fields is required for the quantitative assessment of urban wind environments. Computational fluid dynamics (CFD) is a powerful tool for modeling the wind flow characteristics in urban areas. Although CFD has been widely used in various fields, its use for simulating urban wind fields has limitations because of the complexity of urban building models and the high computational workload. Accordingly, we consider the generalization parameters in the vertical and horizontal directions based on the CFD results and the building topology based on the state of the building nodes. We perform a two-dimensional generalization of building clusters, conduct spatial analysis in a geographic information system (GIS), and generate three-dimensional models. This generalization scheme is applied to Meiling Street in Jinjiang City, Fujian Province, China. The results indicate that the generalization decreases the number of buildings from 7003 to 3367 and the computation time from 11 h and 26 min to 10 h and 25 min. The computation efficiency is improved by 8.89%, with 1.85% changes in the average wind speed ratio. This scheme substantially improves the computational efficiency of urban wind field CFD simulations by reducing the geometric model’s complexity without compromising the accuracy. This strategy is suitable for simulating large-scale urban wind fields.

High-spatiotemporal-resolution surface temperature data play a crucial role in monitoring urban heat island effects. Compared with Landsat 8, MODIS surface temperature products offer high temporal resolution but suffer from low spatial resolution. To address this limitation, a two-step downscaling model (TSDM) was developed in this study for MODIS surface temperature by leveraging random forest (RF) algorithms. The model integrates remote sensing data, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI), alongside the land cover type, digital elevation model (DEM), slope, and aspect. Additionally, a water surface temperature fitting model (RF-WST) was established to mitigate the issue of missing data over water bodies. Validation using Landsat 8 data reveals that the average out-of-bag (OOB) error for the RF-250 m model is 0.81, that for the RF-WST model is 0.73, and that for the RF-30 m model is 0.76. The root mean square error (RMSE) for all three models is below 1.3 K. The construction of the RF-WST model successfully supplements missing water body data in MODIS outputs, enhancing spatial detail. The downscaling model demonstrates strong performance in grassland areas and shows robust applicability during winter, spring, and autumn. However, due to a half-hour temporal discrepancy in the validation data during the summer, the model exhibits reduced accuracy in that season.

Solar diffuse radiation (DIFRA) is an important component of solar radiation, but current research into the estimation of DIFRA is relatively limited. This study, based on remote sensing data, topographic data, meteorological reanalysis materials, and measured data from radiation observation stations in Chongqing, combined key factors such as the solar elevation angle, water vapor, aerosols, and cloud cover. A high-precision DIFRA estimation model was developed using the random forest algorithm, and a distributed simulation of DIFRA in Chongqing was achieved. The model was validated using 8179 measured data points, demonstrating good predictive capability with a correlation coefficient (R²) of 0.72, a mean absolute error (MAE) of 35.99 W/m², and a root mean square error (RMSE) of 50.46 W/m². Further validation was conducted based on 14 radiation observation stations, with the model demonstrating high stability and applicability across different stations and weather conditions. In particular, the fit was optimal for the model under overcast conditions, with R² = 0.70, MAE = 32.20 W/m², and RMSE = 47.51 W/m². The results indicate that the model can be effectively adapted to all weather calculations, providing a scientific basis for assessing and exploiting solar energy resources in complex terrains.