In so far,the post-seasonal best track datasets given by each regional center are used as the true values of tropical cyclone forecast performance verification and analysis.Operationally,the verification regards real-time positioning as the true values as a result of the best track datasets published after one year,which leads to an obvious inevitable error.Consequently,different organizations give out distinct forecast performance,which causes confusions.In order to analyze the bias in forecast performance verifications by using the real-time/initial positioning and the best track datasets as the "true values" respectively and assess the possible effect of positioning error on forecast performance,this paper firstly investigates the bias between the best track datasets and the real-time/initial positioning (i.e.the positioning error) and its distribution characteristics,then analyzes the difference when using the real-time/initial positioning and the best track datasets as the "true values" to calculate the forecast errors respectively,and finally preliminarily assesses the sensitivity of forecast performance to positioning error based on the most basic CLIPER (Climatology and Persistence) forecast method.Results show that when the best track datasets from CMA-STI are taken as the "true values",the average positioning error is 24.3 km from 2013 to 2019,and when the datasets from RSMC-Tokyo are taken as the "true values",the error is 26.2 km.It found that the positioning error is closely related to the intensity.The positioning error in tropical storm stage is 35.7-41.1 km,while that in super typhoon stage is only 7.5-8.3 km.Within 96 h lead time,the average forecast error calculated with the best track datasets as the "true values" is slightly less than that calculated with the real-time/initial positioning as the "true values",but the stronger the intensity,the smaller the difference.The positioning error has a significant impact on the forecast performance in a short lead time.