Diagnostic study of the forecast accuracy of the ensemble models at the recurvature point of the typhoon

Climatologically, typhoons are generated over the western North Pacific (WNP) and move towards the East Asian countries, such as China, Korea, and Japan, etc. The typical track of typhoons in this case is a C-type curve, and they change their track over the ocean in the midlatitude. This turning point of the track of typhoon is called the recurvature point. However, the untypical track typhoon frequently occurred in recent years and the track forecast is difficult.
For example, the 6th typhoon Kanun in 2023, made two sharp recurvature while moved to the north. Kanun has an increase in track error in the early and mid-stage of prediction, which is estimated to be an error caused by the inability to accurately predict the two recurvature point. The maximum error of the models was approximately 400km for the 72-hour forecast and approximately 700km for the 120-hour forecast, respectively. This is larger than the average error of other typhoons in 2023 as well as the overall error average of Kanun. The question of why is the Kanun’s track error is larger than average is raised.
This study is motivated in order to explain the reason of unusual large track error of typhoons. In the first step, we investigate the two unusual track typhoons errors such as, 11th typhoon Hinnamnor in 2022 and 6th typhoon Khanun in 2023. The model track error will be analyzed by calculating the error at the recurvature point of 102 members of the global ensemble model of KIM (Korean Integrated Model, 26 members), UM (Unified Model, 25 members), and ECMWF (The European Centre for Medium-Range Weather Forecasts, 51 members), respectively. 
The individual prediction data of the ensemble members rather than the ensemble mean is used. The recurvature point is automatically calculated from the predicted track at each issue time and compared with the recurvature point calculated on the best track. The prediction trend and performance of the ensemble model are identified by analyzing the distribution of the forecast time error and forecast position error at the recurvature point. Furthermore, the prediction field is classified and diagnosed according to the forecast error.