How End-User Metadecisions Drive Economic Value and Why It Matters

Abstract The extrascientific value of a weather forecast depends on how it is used in decisions. To explore a wide variety of possible use cases, we propose a notional formula for aggregate economic value based on the canonical cost:loss model. Using this framework, we identify characteristics of high-value forecasts and use cases and show how the distribution of these characteristics may be shaped by end users’ metadecisions—earlier decisions that affect use-case parameters that, in turn, determine forecasts’ economic value. Because aggregate economic value depends on which use cases are most prevalent, we analyze how common verification metrics implicitly weight different forecasts and use cases. This analysis can help forecasting organizations identify high-value use cases and end users and inform the development and selection of verification metrics for communication with end users and in forecast model and product development. Significance Statement The fundamental purpose of weather forecasts is to inform end users so they can better adapt to weather. The value of weather forecasts depends on both forecast accuracy and how well forecasts align with end users’ use cases. This paper examines how end-user decisions, including longer-term decisions made before receipt of a specific forecast, affect forecast value and identifies characteristics of use cases that are likely to be most prevalent. In addition, this paper shows how common verification metrics implicitly weight different forecast use cases and analyzes how this aligns with their contribution to aggregate economic value. Such metrics are gaining even more importance due to their role in developing data-driven weather models. These findings suggest the types of use cases the weather community should target for outreach and criteria for developing and selecting verification metrics.