文献类型：期刊文献

英文题名：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

作者：Li, Jiajing[1];Zhang, Yu[1];Chen, Siqi[1];Shao, Duanzhou[1];Hu, Jiazheng[1];Feng, Junjie[1];Tan, Qichang[1];Wu, Deping[2];Kang, Jiaqi[3]

机构：[1]Guangdong Ocean Univ, Coll Ocean & Meteorol, Zhanjiang 524088, Peoples R China;[2]China Meteorol Adm, Zhanjiang Meteorol Bur, Zhanjiang 524005, Peoples R China;[3]China Meteorol Adm, Meteorol Observat Ctr, Beijing 100081, Peoples R China

年份：2023

卷号：14

期号：9

外文期刊名：ATMOSPHERE

收录：SCI-EXPANDED(收录号：WOS:001075902800001)、、EI(收录号：20234014828657)、Scopus(收录号：2-s2.0-85172928185)、WOS

基金：I would like to express my sincere gratitude to the following individuals for their valuable contributions to this research project. Zhang provided invaluable guidance and mentorship throughout the entire process, from the initial conceptualization to the final revision. I am also grateful to my research participants in the 'WE-HATE-NWP' group for their willingness to share their insights and experiences. Finally, I would like to thank my family and friends for their unwavering support and encouragement throughout this journey. We also want to express our sincere gratitude for the time and effort invested by all the reviewers and editors in evaluating our work. We truly appreciate the constructive criticism and insightful suggestions that have been provided, which have undoubtedly contributed to the enhancement of the manuscript.

语种：英文

外文关键词：NWP; data assimilation; typhoon forecasting; Minimum Covariance Determinant; one-class Support Vector Machine

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