On the Combination of Physical Parameterization Schemes for Tropical Cyclone Track and Intensity Forecasts in the Context of Uncertainty

The selection of physical parameterization schemes for tropical cyclone (TC) forecasts has required a substantial amount of effort. In general, the evaluation of physical parameterization schemes and their combined performance was based solely on the deterministic forecast, which had inherent limitations in representing the overall performance of physical parameterization schemes due to the model uncertainty. This study introduces an uncertainty-informed framework of evaluating and selecting the combination of physical parameterization schemes for TC forecasts, based on the ensemble forecast that could include the model uncertainty roles. The performance ranking of the scheme combination based on the ensemble mean error is found to be distinct from that based on the deterministic forecast error. Moreover, differences in both ensemble mean errors and ensemble spreads for various scheme combinations highlight the importance of considering two metrics concurrently, that is, via the quality of the forecast distribution as a whole, to assess the forecast performance. Consequently, the ensemble Continuous Ranked Probability Score (eCRPS) is used to quantify the performance of the scheme combinations, and it is demonstrated that the performance is more comprehensive than that in the deterministic context. Finally, the well-performed scheme combination for the forecasts of six intense TCs is chosen from the evaluated schemes in the context of model uncertainty, based on the overall quality of TC track and intensity forecast distributions.Plain Language Summary In order to improve the accuracy of TC track and intensity forecasts, it is crucial to select the appropriate physical parameterization schemes for the forecasts. In general, the performance of the physical scheme was quantified by comparing the observation with a single forecast value. Taking into account the non-negligible uncertainty sources in the forecast that contribute to the final errors, the forecast value with the chosen physical scheme will be a distribution rather than a point. Through a type of ensemble perturbation, this study restores some model uncertainty information and evaluates the pre-selected combinations of physical parameterization schemes in the ensemble forecasts. The performances of the scheme combinations based on the ensemble mean error differ from those based on the single forecast error. In addition to the differences in ensemble mean errors, there are also differences in the forecast distributions of various scheme combinations. Thus, in the context of uncertainty, the performances of the scheme combinations are quantified by the overall quality of the forecast distribution and shown to be more comprehensive than in the deterministic context. Finally, the well-perfor med scheme combination for both TC track and intensity forecasts of six intense TCs is quantitatively selected from the evaluated schemes.