A comprehensive quality control algorithm for atmospheric motion vectors and its impacts on analysis and forecast of the China Meteorological Administration Global Forecast System

Abstract Atmospheric motion vector (AMV) data play a crucial role in improving the forecast skills of models. Quality control is an essential step that affects the assimilation effectiveness of the AMVs. In a bid to rectify the deviation issue of the initial (analysis) atmospheric dynamic field in the troposphere and to improve AMV assimilation and the forecast skill of the China Meteorological Administration Global Forecast System (CMA‐GFS), a comprehensive quality control (CQC) method is proposed in this study. The CQC encompasses a quality indicator check and blacklist establishment for the AMVs, as well as the background field check based on biweight statistics in the bivector space. Detailed analysis of AMV data eliminated by CQC steps reveals its effectiveness in excluding high and low wind speed outliers, normalizing the probability density function (PDF) of observation‐minus‐background (O − B) values to a Gaussian distribution, thus resolving leptokurtic and platykurtic distribution issues. A 35‐day assimilation sensitivity experiment shows that the original quality control (OQC) increases analysis and forecast errors, especially in tropical wind fields by over 10%. In contrast, CQC significantly reduces these errors, with an average tropospheric dynamic field error decrease of over 3% in the Southern Hemisphere. The CQC's biweight method in bivector space demonstrates potential for quality control of atmospheric dynamic field observations.