Strong and highly variable winds and gusts are major hazards to infrastructure, properties, and life. Consequently, accurate prediction and timely detection of wind gust intensity have always been a focus of interest for earth scientists and weather forecasters.

In this study, The WRF (Weather Research and Forecasting) post-process diagnostic of wind gusts (WPD method) was utilized to predict non-convective wind gust speeds using the direct outputs of the WRF model. To improve the prediction accuracy of this method, the results were post-processed using an artificial neural network (ANN). Multiple different ANN algorithms were examined to achieve the most accurate predictions possible. The results were evaluated using observational data extracted from 32 synoptic stations across Iran during the time period from 2014 to 2018.

The results indicate that employing a multilayer perceptron ANN with a hybrid structure, consisting of one input layer comprising five parameters (10 m wind speed, sea level pressure, temperature, relative humidity, and predicted wind gust speed obtained from the WPD method), one hidden layer with a sigmoid activation function and 12 neurons, one output layer with a linear activation function and using the BR (Bayesian Regularization) training algorithm, significantly improve the accuracy of the WPD wind gust speed prediction method. The RMSE for wind gust speed prediction has decreased from 3.68 m/s (WPD method) to 1.88 m/s for the validation dataset. Additionally, there were considerable improvements of 50 %, 74 %, and 17 % in the MAE, MSE, and R², respectively.

强而多变的风和阵风对基础设施、财产和生命构成重大危害。因此，准确预测和及时检测阵风强度一直是地球科学家和天气预报人员关注的焦点。

在本研究中，利用 WRF（天气研究和预报）阵风后处理诊断（WPD 方法），利用 WRF 模型的直接输出来预测非对流阵风速度。为了提高该方法的预测精度，使用人工神经网络（ANN）对结果进行后处理。检查了多种不同的 ANN 算法，以实现最准确的预测。结果是使用 2014 年至 2018 年期间从伊朗各地 32 个气象站提取的观测数据进行评估的。

结果表明，采用混合结构的多层感知器 ANN，由一个输入层组成，该输入层包含 5 个参数（10 m 风速、海平面压力、温度、相对湿度和通过 WPD 方法获得的预测阵风速度），一个隐藏层具有sigmoid激活函数和12个神经元，输出层具有线性激活函数并采用BR（贝叶斯正则化）训练算法，显着提高了WPD阵风风速预测方法的准确性。验证数据集的阵风速度预测的 RMSE 从 3.68 m/s（WPD 方法）下降到 1.88 m/s。此外，MAE、MSE 和 R ²分别显着提高了 50%、74% 和 17%。