# Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields
## 1 Introduction
However, using this method for predicting high-accuracy wind fields requires large computational resources, which are currently unattainable [12]. The second catalogue is based on historical wind field data. The used historical wind field data can be from weather prediction models, e.g., GraphCast [13], PanguWeather [14], and FourCastNet [15]. The models are trained based on the reproduction weather data, ERA5. The short-term and long-term global weather prediction can be performed with high speed, high accuracy, and low loss. However, the mentioned models may perform the weather data prediction with 0.25\({}^{\circ}\) of the highest spatial resolution and one hour of the shortest time step, which are limited by the resolution of ERA [14]. The historical data can be also from observation stations and lidars. The machine learning methods such as Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), graph neutral networks, Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRUs) are used to realize weather predictions, employing data from observation stations and lidars [16; 17; 18; 19; 20]. It is hard to predict the three-dimensional wind field on a small scale using the observation station outputs due to their limited resolution and the limited number of the stations. On the other hand, although laser radar scanned data have a higher resolution, it is difficult to obtain a three-dimensional wind field from single-angle plan position indicator (PPI) scanned data using wind field inversion methods. Conducting multiple PPI scans of the environmental wind field may introduce a certain time delay, which reduces the real-time performance and accuracy of the three-dimensional predicted wind field obtained through inversion. In summary, it is hard to predict the small-scale wind field precisely with ordinary computer resources based on the above-mentioned methods. Meanwhile, several stud ies have demonstrated the advantages of the WRF-CFD coupled method in simulating small-scale wind fields with high accuracy and precision [5; 6; 7; 8]. However, most of the relevant work focuses on the data processing of the historical wind fields, while the output variables of the WRF model are directly used as the inputs to the CFD model for the simulation of small-scale wind fields. In this paper, we propose the ConvLSTM-DSConvGRU neutral network model to predict the wind speed and temperature with high speed. The prediction involves a limited number of parameters and does not cost much in terms of computer resources. The predicted wind speed and temperature are fed to the CFD model as the boundary conditions [21]. Consequently, the CFD model implements the wind field simulations and the predicted wind fields are obtained. In this study, Hong Kong International Airport with a complex geographical location and wind field environment was selected as the research object, and a short-term prediction method for small-scale three-dimensional wind fields based on the coupling of WRF and CFD was proposed. First, the wind field simulation of the complex terrain area surrounding the Hong Kong International Airport was performed using the WRF-CFD coupled model. The wind speed and temperature information obtained from the WRF simulation were used to provide realistic boundary conditions for the CFD model. Then, the impacts of the nearest neighbour interpolation and inverse distance weighted interpolation on the CFD simulation results were compared and analysed to verify the accuracy and applicability of the numerical simulation. Finally, depthwise separable convolutions were introduced, and an encoder-decoder network model based on a hybrid of ConvLSTM and a depthwise separable ConvGRU was proposed. This model predicted the WRF output variables used as boundary conditions for the CFD model. The model performance was evaluated using error evaluation indicators.