WMO HIWeather/SERA Value Chain Project: Using Value Chain Approaches to Evaluate End-to-End Warning Systems

The weather information value chain provides a framework for characterising the production, communication, and use of information by all stakeholders in an end-to-end warning system covering weather and hazard monitoring, modelling and forecasting, risk assessment, communication and preparedness activities. Warning services are typically developed and provided through a multitude of complex and malleable value chains (networks), often established through co-design, co-creation and co-provision.   

A 4-year international project under the WMO World Weather Research Programme is using value chain approaches to describe and evaluate warning systems for high impact weather by integrating physical and social science. It aims to create a framework with guidance and tools for using value chain approaches, and to develop a database of high impact weather warning case studies for scientists and practitioners to review, analyse and learn from previous experience using value chain approaches. 

Since its start in November 2020, the project has made significant progress to achieve its aims through contributions from a continuously growing, interdisciplinary project team. It has developed an interim database template for high-impact weather event case study collection and analysis and used it to describe and learn from several high impact weather events that happened in 2021. It has also created a glossary for a common terminology between social and physical scientists and outlined a conceptual high-level value chain framework. 

Here we focus on the interim database template that provides a tool for scientists and practitioners involved in researching, designing and evaluating weather-related warning systems to review previous experience of high impact weather events and assess their efficacy. The interim database leverages and extends existing databases, such as EM-DAT, ECMWF Severe Event Catalogue, SHELDUS, DesInventar, etc., collecting rich information on the many components of the warning value chain. It will enable in-depth case studies and cross-cutting analysis of end-to-end warning value chains, from simple to complex, to understand effective practices, and support the value cycle of review and learning from past events to identify improvements that would enhance future warnings. 

We encourage the research and operational community to participate in this project by contributing case studies of high impact events and collaborating in their analysis.  

 

Corresponding/presenting author: David Hoffmann, Bureau of Meteorology, Melbourne, Australia; david.hoffmann@bom.gov.au