01 Nov 2023
 | 01 Nov 2023
Status: this preprint is open for discussion.

Accounting for Uncertainties in Forecasting Tropical Cyclone-Induced Compound Flooding

Kees Nederhoff, Maarten van Ormondt, Jay Veeramony, Ap van Dongeren, Jose Antolínez, Tim Leijnse, and Dano Roelvink

Abstract. Tropical cyclone impacts can have devastating effects on the population, infrastructure, and on natural habitats. However, predicting these impacts is difficult due to the inherent uncertainties in the storm track and intensity. In addition, due to computational constraints, both the relevant ocean physics and the uncertainties in meteorological forcing are only partly accounted for. This paper presents a new method, called the Tropical Cyclone Forecasting Framework (TC-FF), to probabilistically forecast compound flooding induced by tropical cyclones, considering uncertainties in track, forward speed, and wind speed/intensity. The open-source method accounts for all major relevant physical drivers, including tide, surge, and rainfall, and considers TC uncertainties through Gaussian error distributions and autoregressive techniques. The tool creates temporally and spatially varying wind fields to force a computationally efficient compound flood model, allowing for the computation of probabilistic wind and flood hazard maps for any oceanic basin in the world, as it does not require detailed information on the distribution of historical errors. A comparison of TC-FF and JTWC operational ensembles, both based on DeMaria et al. (2009), revealed minor differences of <10 %, suggesting that TC-FF can be employed as an alternative, for example, in data-scarce environments. The method was applied to Cyclone Idai in Mozambique. The underlying physical model showed reliable skill in terms of tidal propagation, reproducing the storm surge generation during landfall and flooding near the city of Beira (success index of 0.59). The method was successfully applied to forecast the impact of Idai with different lead times. The case study analyzed needed at least 200 ensemble members to get reliable water levels and flood results three days before landfall (<1 % flood probability error and <20 cm sampling errors). Results showed the sensitivity of forecasting, especially with increasing lead times, highlighting the importance of accounting for cyclone variability in decision-making and risk management.

Kees Nederhoff et al.

Status: open (until 27 Dec 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-2341', Anonymous Referee #1, 04 Dec 2023 reply

Kees Nederhoff et al.

Kees Nederhoff et al.


Total article views: 144 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
89 47 8 144 5 6
  • HTML: 89
  • PDF: 47
  • XML: 8
  • Total: 144
  • BibTeX: 5
  • EndNote: 6
Views and downloads (calculated since 01 Nov 2023)
Cumulative views and downloads (calculated since 01 Nov 2023)

Viewed (geographical distribution)

Total article views: 143 (including HTML, PDF, and XML) Thereof 143 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 06 Dec 2023
Short summary

Forecasting tropical cyclones and their flooding impact is challenging. Our research introduces the Tropical Cyclone Forecasting Framework (TC-FF), enhancing cyclone predictions despite uncertainties. TC-FF generates global wind and flood scenarios, valuable even in data-limited regions. Applied to cases like Cyclone Idai, it showcases potential in bettering disaster preparation, marking progress in handling cyclone threats.