Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding

The prediction of tropical cyclone impacts (extreme winds and flooding) is challenged by uncertainties in the storm's trajectory, intensity and timing, compounded by computational constraints that only partially consider relevant ocean physics and meteorological uncertainties. Thus, information about the variability of an impending impact is crucial for decision-makers.

This presentation introduces the Tropical Cyclone Forecasting Framework (TC-FF), an innovative open-source method for the probabilistic forecasting of compound flooding resulting from tropical cyclones. TC-FF accounts for critical physical drivers, including tide, surge, and rainfall, and utilizes Gaussian error distributions and autoregressive techniques to consider tropical cyclone uncertainties. In particular, our approach integrates a TC emulator using a Monte Carlo-based ensemble sampling generation with an autoregressive technique, which is a simplified adaptation of DeMaria et al. (2009). The framework creates variable wind fields to power an efficient compound flood model, enabling the generation of probabilistic wind and flood hazard maps for any oceanic basin globally, independent of detailed historical error data.

This presentation will discuss the methodology, demonstrate a comparative assessment with JTWC operational ensembles, and present a case study application of Cyclone Idai that hit Beira, Mozambique. A comparison of TC-FF and JTWC operational ensembles revealed minor differences in along-track, cross-track and intensity variations of <10%, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. Applying this method to Cyclone Idai proved the model's reliability in simulating tidal propagation, storm surge creation, and localized flooding near Beira. The case study required a minimum of 200 ensemble members to attain reliable water levels and flood outcomes three days before landfall. Key findings illustrate the forecasting sensitivity, particularly at increased lead times, emphasizing the necessity of cyclone variability consideration in short-term forecasting, operational risk analysis, and decision-making. The proposed TC-FF method offers a robust tool for enhanced forecasting and management of tropical cyclone-induced flooding.