Research on Short-Term Prediction Methods for Small-Scale Three-Dimensional Wind Fields

Abstract

The accurate prediction of small-scale three-dimensional wind fields is of great practical significance for aviation safety, wind power generation, and related fields. This study proposes a novel method for predicting small-scale three-dimensional wind fields by combining the mesoscale Weather Research and Forecasting (WRF) model with Computational Fluid Dynamics (CFD). The method consists of three components: the WRF module, the hybrid neural network prediction module, and the CFD module. First, mesoscale meteorological fields are simulated using the WRF module to establish a historical inflow boundary dataset for the CFD domain. Next, deep separable convolutions are incorporated, and convolutional long short-term memory (ConvLSTM) is combined with a deep separable convolution-gated recurrent unit (DSConvGRU) to construct a hybrid neural network prediction module named ConvLSTM-DSConvGRU. This module is employed for predicting inflow boundary data. Finally, the predicted inflow boundary conditions drive the CFD module to predict small-scale three-dimensional wind fields. The effectiveness of the WRF and CFD downscaling coupling method was validated using observed data from meteorological stations within the simulated domain, along with statistical indicators of errors. Additionally, a comparative evaluation was conducted between the proposed hybrid network model and the four commonly used spatiotemporal prediction models to assess its prediction performance. The results demonstrate that our proposed wind field prediction method achieves accurate simulation and short-term prediction of small-scale three-dimensional wind fields, and the hybrid network model exhibits comprehensive advantages in terms of model complexity and prediction accuracy.