A warning value chain Assessment for Typhoon Muifa (2022)

Typhoon Muifa (2022) is the strongest landfalling typhoon over China in 2022, causing long-lasting and widespread wind and rainfall in East and Northeast China. Using the end-to-end warning value chain method, this paper carried out the evaluations on the warning chain including observation, weather forecast, hazard forecast, impact forecasts, warning communication and warning response for this case. The “bridges” between each part are comprehensively analyzed based on the expert scores. In the four times of landfall events, monitoring, forecasting and early warning in the field of meteorology is relatively done well. The medium- to long-term track forecast in the early stage of Muifa’s lifetime is a great challenge in the track forecast, and the deviation between the numerical model outputs and observations in the long-term forecast of the main impact weather systems is obvious, timely inspection and correction of the model results is very important for the warning preparing. Under the scenario of binary typhoons or multi-vortex, the dispersion of the ensemble forecast is large. Intensity and position differences of the cyclone on the east side have a significant impact on the track of Muifa. The along-track error in the process of extratropical transition after landfall is the primary source of large track forecast errors, especially reduced the effectiveness of early warnings. It is also found out the weakest link among the chain is the impact forecast, with deficiencies in impact data sharing among different ministries and accurate high-resolution modeling in social, economic and health fields. Under the national disaster prevention and mitigation framework, meteorological departments have established effective warning releasing system for the decision-makers. Timely warnings have been delivered at different government levels and there is zero casualties for this case. Attention should be paid to the analysis of extreme tropical cyclone disasters and their trends in the context of climate change, as well as the refinement and development of impact forecasting and risk warning product.This assessement of the warning value chain provide a good example for other meteorological service to conduct similar analysis of their own warning chains.