Optimizing Clear Air Turbulence Forecasts Using the K-nearest Neighbor Algorithm

The complexity and unpredictability of clear air turbulence (CAT) pose significant challenges to aviation safety. Accurate prediction of turbulence events is crucial for reducing flight accidents and economic losses. However, traditional turbulence prediction methods, such as ensemble forecasting techniques, have certain limitations: they only consider turbulence data from the most recent period, making it difficult to capture the nonlinear relationships present in turbulence. This study proposes a turbulence forecasting model based on the K-nearest neighbors (KNN) algorithm, which uses a combination of eight CAT diagnostic features as the feature vector and introduces CAT diagnostic feature weights to improve prediction accuracy. The model calculates the results of seven years of CAT diagnostics from 125 to 500 hPa obtained from the ECMWF fifth-generation reanalysis dataset (ERA5) as feature vector inputs and combines them with the labels of Pilot Reports (PIREP) annotated data, where each sample contributes to the prediction result. By measuring the distance between the current CAT diagnostic variable and other variables, the model determines the climatically most similar neighbors and identifies the turbulence intensity category caused by the current variable. To evaluate the model’s performance in diagnosing high-altitude turbulence over Colorado, PIREP cases were randomly selected for analysis. The results show that the weighted KNN (W-KNN) model exhibits higher skill in turbulence prediction, and outperforms traditional prediction methods and other machine learning models (e.g., Random Forest) in capturing moderate or greater (MOG) level turbulence. The performance of the model was confirmed by evaluating the receiver operating characteristic (ROC) curve, maximum True Skill Statistic (maxTSS = 0.552), and reliability plot. A robust score (area under curve: AUC = 0.86) was obtained, and the model demonstrated sensitivity to seasonal and annual climate fluctuations.