One crucial mechanism in the spread of wildfires is the so-called fire-spotting: a random phenomenon that occurs when embers are transported over large distances. Fire-spotting speeds up the rate of spread and starts new ignitions that can jeopardise firefighting operations. Unfortunately, operational fire-spread simulators may not account for spotting events, thus overlooking the harmful consequences associated with this phenomenon. In this work, three fire spotting parametrisations are integrated in the operational wildfire simulator PROPAGATOR based on Cellular Automata (CA). RandomFront, a physics-based parametrisation of fire-spotting, is tested for the first time in the context of CA simulators. RandomFront is compared with other two parametrisations already adopted in CA based simulators, those by Alexandridis and co-authors and by Perryman and collaborators. A wildfire occurred in the summer of 2021 in the municipality of Campomarino (Molise, Italy), and where spotting effects were clearly reported, is used as a case study. This case study, featuring evident airborne transport of firebrands, paves the way for a framework for comparing parameterised spotting models used in operational scenarios. RandomFront produced a more complex burning probability pattern than the other parametrisations and it predicted a higher probability of burning in the zone mainly affected by the fire-spotting
