首页|Optimizing Clear Air Turbulence Forecasts Using the K-Nearest Neighbor Algorithm
Optimizing Clear Air Turbulence Forecasts Using the K-Nearest Neighbor Algorithm
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国家科技期刊平台
NETL
NSTL
万方数据
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,tradi-tional 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 neighbor(KNN)algorithm,which uses a combination of eight CAT diagnostic features as the feature vector and introduces CAT diagnostic fea-ture 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 predic-tion result.By measuring the distance between the current CAT diagnostic variable and other variables,the model de-termines the climatically most similar neighbors and identifies the turbulence intensity category caused by the cur-rent 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 the curve:AUC=0.86)was obtained,and the model demon-strated sensitivity to seasonal and annual climate fluctuations.
clear air turbulenceK-nearest neighbor(KNN)algorithmthe ECMWF fifth-generation reanalysis data-set(ERA5)turbulence prediction
Aoqi GU、Ye WANG
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College of General Aviation and Flight,Nanjing University of Aeronautics and Astronautics,Nanjing 210016
国家科技期刊平台
NSTL
万方数据
