Since the definition of errors of each dataset and assigning weights to them greatly affects the forecast results in the data assimilation products, we can divide the types of errors as measurement errors, and representation errors. Furthermore, variables that increase uncertainty in typhoons—such as strong winds, clouds, and ocean mixing—have clear contributions, and there are limitations in fully representing this phenomenon within the model. Therefore, this study aims to analyze the structure and causes of error generation and weight adjustment, as well as to examine their impacts on real-world dynamics.