Hannah Brown

There is a growing need for more systematic, robust, and comprehensive information on the value-add of climate services from both the demand and supply sides. There is a shortage of published value-add assessments that focus on the decision-making context, involve participatory or co-evaluation approaches, avoid over-simplification, and address both the quantitative (e.g., economic) and qualitative (e.g., social) values of climate services. The 12 case studies that formed the basis of the European Union-funded SECLI-FIRM project were co-designed by industrial and research partners in order to address these gaps while focusing on the use of tailored sub-seasonal and seasonal forecasts in the energy and water industries. For eight of these case studies, it was possible to apply quantitative economic valuation methods: econometric modelling was used in five case studies while three case studies used a cost/loss (relative economic value) analysis and avoided costs. The case studies illustrated the challenges in attempting to produce quantitative estimates of the economic value-add of these forecasts. At the same time, many of them highlighted how practical value for users—transcending the actual economic value—can be enhanced; for example, through the provision of climate services as an extension to their current use of weather forecasts and with the visualisation tailored towards the user.

For many businesses, the weather is a strong driver of performance. Here, we introduce two assessment tools for organisations wanting to identify these links, so that they may subsequently be used to predict future changes. By combining data collected by businesses with historical weather data, the assessment tools use a set of pre‐defined statistical analysis methods to quantify their particular sensitivities. This analysis is fast and flexible, to facilitate the ease of incorporation of meteorological effects into the corporate decision‐making process. The weather sensitivity analysis conducted includes both correlation and regression analysis and weather pattern analysis, providing results suitable for subsequent operational application within a real‐time forecast system. A demonstration is provided for bike hire data collected under the Santander Cycles scheme, published by Transport for London, wherein it is shown that 74% of the variability in the journeys undertaken may be explained by a simple statistical model involving only two weather variables (temperature and rainfall).