Using ensembles to analyse predictability links in the tropical cyclone flood forecast chain

Abstract

Fluvial flooding is a major cause of death and damages from tropical cyclones (TCs), so it is important to understand the predictability of river flooding in TC cases, and the potential of global ensemble flood forecast systems to inform warning and preparedness activities. This paper demonstrates a methodology using ensemble forecasts to follow predictability and uncertainty through the forecast chain in the Global Flood Awareness System (GloFAS), to explore the connections between the skill of the TC track, intensity, precipitation and river discharge forecasts. Using the case of Hurricane Iota, which brought severe flooding to Central America in November 2020, we assess the performance of each ensemble member at each stage of the forecast, along with the overall spread and change between forecast runs, and analyse the connections between each forecast component. Strong relationships are found between track, precipitation and river discharge skill. Changes in TC intensity skill only result in significant improvements in discharge skill in river catchments close to the landfall location that are impacted by the heavy rains around the eye wall. The rainfall from the wider storm circulation is crucial to flood impacts in most of the affected river basins, with a stronger relationship with the post-landfall track error rather than the precise landfall location. We recommend the wider application of this technique in TC cases, to investigate how this cascade of predictability varies with different forecast and geographical contexts, to help inform flood early warning in TCs.