Comparing Quality Control Procedures Based on Minimum Covariance Determinant and One-Class Support Vector Machine Methods of Aircraft Meteorological Data Relay Data Assimilation in a Binary Typhoon Forecasting Case

This study investigates the impact of assimilating Aircraft Meteorological Data Relay (AMDAR) observations on prediction two typhoons, Nesat and Haitang (2017), using Gridpoint Statistical Interpolation (GSI) assimilation system Weather Research Forecasting (WRF) model. Two quality control (QC) methods, Minimum Covariance Determinant (MCD) one-class Support Vector Machine (OCSVM), were employed to perform QC AMDAR before data assimilation. The results indicated that both methods significantly reduced kurtosis, skewness, discrepancies between reanalysis data. distribution after applying MCD-QC method exhibited a closer resemblance Gaussian distribution. Four numerical experiments conducted assess different qualities typhoon forecasting, including experiment without (EXP-CNTL), all (EXP-RAW), (EXP-MCD), OCSVM-QC (EXP-SVM). demonstrated in improved track intensity typhoons. Furthermore, utilizing enhanced performance forecasting prediction, with EXP-MCD showing best performance. As for three varying strengths weaknesses at times, smaller forecast errors average.