Abstract. There remains a gap between the production of scientifically robust forecasts, and the translation of these forecasts into useful information such as daily "bulletins" for decision-makers in early warning systems. There is significant published literature on best practice to communicate risk information, but very little to guide and provide advice on the process of how these bulletins have been, or should be, developed. This paper reviews two case studies where bulletins were developed for national and district-level government agencies and humanitarian responders: daily reports in response to Cyclones Idai and Kenneth in Mozambique, and prototype landslide forecast bulletins in Nilgiris and Darjeeling Districts of India. Primary data was collected from producers and intermediaries of the bulletins via interview, and secondary data analysed on: iterative changes in the bulletin development; minutes from internal discussions; and feedback from users to extract learning on both the content and process of developing the bulletins. There were significant similarities in the type of content included in the bulletins, such as the layout, choice of words, and use of visualisation that was consistent with published best practices. Both case studies experienced challenges dealing with uncertainty, complexity, and whether to include advice. There were also similarities in the processes and approaches taken to develop the bulletins. Both case studies took an iterative approach, developed feedback mechanisms, benefitted from experienced multi-disciplinary teams, emphasised the need for strong inter-relationships, and the importance and value of preparedness and protocols. A major challenge was the difficulty of balancing science capabilities with user needs, which did not become significantly easier to deal with given more time availability. The findings indicate that whilst more research is needed into existing or best practice processes to develop content for forecast bulletins, there is an existing body of experiential and intuitive knowledge and learning that already exists but is not yet captured in an appropriate format that could be of significant interest and value to those developing forecast information. This paper goes some way to capturing some of the learning from translating scientific forecasts into useful information, in particular on both the content and the process of developing forecast bulletins for decision-making.
Probabilistic seasonal dengue forecasting in Vietnam using superensembles
