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Landfalling typhoons can cause disasters over large regions. The government and emergency responders need to take measures to mitigate disasters according to the forecast of landfall position, while slight timing error can be ignored. The reliability of operational model forecasts of typhoon landfall position needs to be evaluated beforehand, according to the forecasts and observation of historical cases. In the evaluation of landfalling typhoon track, the traditional method based on point-to-point matching methods could be influenced by the predicted typhoon translation speed. Consequently, the traditional track evaluation method may result in a large track error even if the predicted landfall position is close to observation. The purpose of this paper is to address the above issue using a simple evaluation method of landfalling typhoon track forecast based on the time neighborhood approach. In this new method, the timing error was lessened to highlight the importance of the position error during the landfall of typhoon. The properties of the time neighborhood method are compared with the traditional method based on numerical forecast results of 12 landfalling typhoon cases. Results demonstrated that the new method is not sensitive to the sampling frequency, and that the difference between the time neighborhood and traditional method will be more obvious when the moving speed of typhoon is moderate (between 15–30 km h−1). The time neighborhood concept can be easily extended to a broader context when one attempts to examine the position error more than the timing error. [ABSTRACT FROM AUTHOR]

Tropical cyclone (TC) genesis forecasting is essential for daily operational practices during the typhoon season. The updated version of the Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS) offers forecasters reliable numerical weather prediction (NWP) products with improved configurations and fine resolution. While traditional evaluation of typhoon forecasts has focused on track and intensity, the increasing accuracy of TC genesis forecasts calls for more comprehensive evaluation methods to assess the reliability of these predictions. This study aims to evaluate the effectiveness of the CMA-TRAMS for cyclogenesis forecasts over the western North Pacific and South China Sea. Based on previous research and typhoon observation data over five years, a set of localized, objective criteria has been proposed. The analysis results indicate that the CMA-TRAMS demonstrated superiority in cyclogenesis forecasts, predicting 6 out of 22 TCs with a forecast lead time of up to 144 h. Additionally, over 80% of the total could be predicted 72 h in advance. The model also showed an average TC genesis position error of 218.3 km, comparable to the track errors of operational models according to the annual evaluation. The study also briefly investigated the forecast of Noul (2011). The forecast field of the CMA-TRAMS depicted thermal and dynamical conditions that could trigger typhoon genesis, consistent with the analysis field. The 96-hour forecast field of the CMA-TRAMS displayed a relatively organized threedimensional structure of the typhoon. These results can enhance understanding of the mechanism behind typhoon genesis, fine-tune model configurations and dynamical frameworks, and provide reliable forecasts for forecasters. [ABSTRACT FROM AUTHOR]

Previous studies showed that 4D-Var technique used for data assimilation could be modified for weather control. This study demonstrates the ability of 4D-Var to influence the future path of a tropical cyclone by calculating perturbations in WRF simulation. Given the background error covariance matrix, the initial field is improved by the vortex dynamic initialization technique. Our results show that 4D-Var can be applied to control the trajectory of simulated tropical cyclones by producing "optimal" perturbations. In the numerical simulation experiment of Typhoon Mitag in 2019, after this kind of weather control similar to data assimilation, the tropical cyclone moved obviously, and the damaging wind over the coastline weakened. The prediction results after the initial field modified by 4D-Var have a great change, and the position of the tropical cyclone moved about 0.5° southeastward after assimilation, which misses the southeast coast of China. Moreover, the damaging wind is also weakened. Since the 4D-Var is premised on the assumption that the model is perfect and does not consider the model error, then the research plan to consider model error and introduce new methods is discussed in the paper. [ABSTRACT FROM AUTHOR]