Recommendations to improve the interpretation of globalflood forecasts to support international humanitarianoperations for tropical cyclones

• Autores: Bazo, Juan, Speight, Linda, Stephens, Elizabeth, Hawker, Laurence, Baugh, Calum, Neal, Jeffrey, Cloke, Hannah, Grey, Stephen, Titley, Helen, Marsden, Katherine, Sumner, Tim, Ficchi, Andrea, Prudhomme, Christel, Archer, Leanne, Dambo, Jânio, Dolan, Siobhan, Huhn, Anna Lena, Moschini, Francesca, Savage, James, Smith, Andy, Towner, Jamie, Wanzala, Maureen
• Formato: objeto de conferencia
• Fecha de Publicación: 2025
• Institución: Universidad Tecnológica del Perú
• Repositorio: UTP-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.utp.edu.pe:20.500.12867/14662
• Enlace del recurso: https://hdl.handle.net/20.500.12867/14662; https://doi.org/10.1111/jfr3.12952
• Nivel de acceso: acceso abierto
• Materia: Anticipatory action; Global flood models; International humanitarian operations; Tropical cyclones; https://purl.org/pe-repo/ocde/ford#1.05.09

International humanitarian organisations increasingly turn to forecast teams to support the coordination of efforts to respond to disasters caused by hazards such as tropical cyclones and large-scale fluvial floods. Such disasters often occur where there is limited local capacity or information available to support decision making and so global forecasting capacity is utilised to provide impact-based flood forecast bulletins. A multidisciplinary team joined together to provide forecast bulletins and expertise for such events through the UK Foreign and Commonwealth Development Office (FCDO). This paper captures the successes and challenges from two cyclones: Hurricane Iota in Central America (November 2020) and Cyclone Eloise in Mozambique (January 2021). Recommendations to improve global forecasting systems are made which will benefit the international community of researchers and practitioners involved in disaster prediction, anticipatory action and response. These include the need for additional data and expertise to support the interpretation of global models, clear documentation to support decision makers faced with multiple sources of information, and the development of user relevant metrics to assess the skill of global models. We discuss the value of effective partnerships and improving synergies between global models and local contexts, highlighting how global forecasting can help build local forecasting capability.