# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 1 Introduction  
In recent years, with the rapid advancement of urbanization in China, the importance of three-dimensional wind field prediction has become increasingly prominent owing to the rapid developments in wind power generation, aviation transportation, and other fields. For example, in the field of wind power, factors such as wind speed, wind direction, and atmospheric pressure determine the electricity generation of wind turbines [1]. Meanwhile, for airports, the variation in wind fields has a direct impact on aircraft takeoff, landing, parking, and maintenance operations. The primary cause of most air traffic accidents is severe wind shear or wake flow encountered during the takeoff and landing phases [2, 3]. Therefore, the accurate simulation and prediction of small-scale three-dimensional wind fields can not only better assess wind resources and predict the inflow volume for wind farm sites but also provide critical meteorological information and ensure aviation safety for airports. With the continuous improvement of computational power, many scholars [4, 5, 6, 7] have proposed Computational Fluid Dynamics (CFD) models to improve the spatial resolution of the Mesoscale Weather Research and Forecasting Model (WRF) in wind field research. Asa widely used meteorological simulation software, WRF takes into consideration various physical processes such as atmospheric dynamics, boundary layer processes, turbulent processes, and radiation processes, and it can simulate large-scale meteorological phenomena at the kilometre scale [4]. On the other hand, CFD, as a numerical simulation method for fluid mechanics, can establish high-accuracy real building and terrain models and achieve the simulation of fine flow fields. Several studies have been conducted on the coupling of WRF and CFD through regional nesting to obtain more accurate and refined wind field information. [PERSON] et al. used the output of the WRF model based on the Four-Dimensional Data Assimilation (FDDA) technology as the boundary condition for CFD flow field simulation [5]. They achieved a refined downscaling simulation of complex local wind fields on terrain with the Reynolds-averaged Navier-Stokes (RANS) method, verifying the effectiveness of the multi-scale coupling method in improving the accuracy of wind field simulation. [PERSON] et al. adopted a one-way coupling method between WRF and CFD and proposed a method to maintain mass conservation in the CFD simulation process [6]. The simulated wind speed was close to the measured value, and they successfully reproduced the curvature of the airflow during typhoons. [PERSON] et al. used a large-eddy simulation (LES) based on the data from the WRF model to accurately reproduce the variation trends of wind speed and wind direction in the airspace above airports under the influence of the terrain of Lantau Island [7]. They also effectively predicted the range of wind shear. However, most of the current research on WRF and CFD coupling focuses on the impact of complex terrain on the wind field. In fact, in addition to airflow disturbances caused by terrain, buildings can also cause sudden changes in wind direction for aircraft under certain weather conditions. A numerical simulation study using a nested mesoscale meteorological model and a CFD model showed that buildings can cause significant changes in wind direction and affect aircraft operation [8]. However, the above study only modelled a few buildings near the runway, and the accuracy needs to be improved. According to recent studies, there are mainly two catalogues of methods for wind field prediction [9]. The first catalogue is based on dynamic physical models, which describe variables such as wind, pressure, density, and temperature in the atmosphere using the laws of fluid mechanics and solve or simplify the equations. In addition, data assimilation techniques are combined to predict the state of the atmosphere [10; 11].