Decision-centric approach to Impact Based Forecast verification

Dec 28, 2024 · 5 min read

Introduction

Impact-based forecasting (IBF) is becoming an essential tool for disaster preparedness and risk reduction. As this approach grows, we need to evaluate how well these forecasts support real-world decisions by stakeholders, such as emergency managers, policymakers, and private organizations. Traditional methods of verifying forecasts mainly focus on statistical accuracy, but these may not fully capture the practical value of forecasts. This post explores why it’s important to go beyond conventional verification methods and adopt a decision-centric verification (DCV) framework.

Traditional weather forecast verification tends to focus too much on technical accuracy, like RMSE (Root Mean Square Error) or bias scores. While these metrics are important, they don’t address the real goal: improving decisions. For example, a forecast that is slightly less accurate but better aligned with decision thresholds could still be more valuable. Instead of only asking, “Was the forecast accurate?” we should also ask, “Did this forecast help make better decisions?” or “Which prediction errors affected the quality of decisions most?” Decision-centric verification shifts the focus to outcomes, asking, “Did the forecast lead to actions (e.g., evacuation, resource allocation) that reduced negative impacts or increased positive outcomes?”

Background

Traditional weather and hazard forecast verification faces several challenges when applied to IBF:

  1. It’s hard to collect detailed impact data for validation.
  2. There’s no easy way to measure how forecast accuracy affects real-world outcomes.
  3. It’s difficult to assess whether the resulting decisions and actions were effective.
  4. The focus is often more on model performance than on practical usefulness.

Literature Review

1. Anticipatory Action Literature

Research on anticipatory action highlights a shift toward DCV in weather and climate forecasting. Early studies on using probabilistic forecasts for water management [1] and more recent work on impact-based verification [2] show how these methods have evolved. Studies like [3] and [4] focus on bridging the gap between traditional weather metrics and real-world applications. Humanitarian organizations [5] and drought forecasting systems [6, 7] have adapted verification methods to align with decision timelines, action triggers, and institutional learning. This research emphasizes the importance of making verification relevant to decision-makers, balancing scientific accuracy with practical needs in disaster response.

2. Bayesian Philosophy

Bayesian methods are crucial for managing uncertainty and improving decisions. Bayesian networks, as demonstrated in [8], structure complex relationships between forecasts, impacts, and outcomes. These networks help address DCV challenges by modeling dependencies between predictors and handling incomplete data. Similarly, Russell’s open-universe probability models [9] offer a way to analyze dynamic scenarios, treating them as “possible worlds” and mapping their causal relationships. Friston’s Active Inference framework [10] uses Bayesian updates to continuously improve decision-making under uncertainty, providing a solid foundation for integrating forecasts, impact assessments, and verification metrics.

3. Event-Based Storylines

Event-based storylines provide a framework for connecting climate science to decision-making under uncertainty. Proposed by [11], this method links climate projections to preparedness decisions. Researchers have expanded it to include measurable socio-economic impacts [12] and practical flood management applications [13]. Further work on storylines [14] has focused on reducing uncertainty and aligning forecasts with stakeholder needs. These storylines map impact chains across different scales, offering a clear, structured way to support scenario-based decisions. They also provide a credible narrative for handling both direct and indirect impacts while maintaining scientific rigor.

The connection between open-universe probability models, “possible worlds,” and event-based storylines is highly complementary for DCV in IBF. Both frameworks handle uncertainty and allow for dynamic scenarios: open-universe models create flexible structures for new entities, while storylines build coherent event chains. Together, they help align forecast reasoning with real-world decision-making, making verification more relevant and useful for stakeholders.

Conclusion

To fully realize the potential of impact-based forecasting, we need to shift from traditional accuracy-focused verification to decision-centric approaches. By leveraging methods like Bayesian frameworks and event-based storylines, we can create more actionable and effective forecasts. These tools help decision-makers focus on what truly matters: reducing risks, saving lives, and improving outcomes.

References

  1. Lopez, Ana, and Sophie Haines. “Exploring the usability of probabilistic weather forecasts for water resources decision-making in the United Kingdom.” Weather, Climate, and Society 9.4 (2017): 701-715.
  2. Busker, Tim, et al. “Impact-based seasonal rainfall forecasting to trigger early action for droughts.” Science of the Total Environment 898 (2023): 165506.
  3. MacLeod, David, Dominic R. Kniveton, and Martin C. Todd. “Playing the long game: Anticipatory action based on seasonal forecasts.” Climate Risk Management 34 (2021): 100375.
  4. Coughlan de Perez, Erin, et al. “Action-based flood forecasting for triggering humanitarian action.” Hydrology and Earth System Sciences 20.9 (2016): 3549-3560.
  5. de la Poterie, Arielle Tozier, et al. “Anticipatory action to manage climate risks: Lessons from the Red Cross Red Crescent in Southern Africa, Bangladesh, and beyond.” Climate Risk Management 39 (2023): 100476.
  6. Guimarães Nobre, Gabriela, et al. “Ready, Set & Go! An anticipatory action system against droughts.” Natural Hazards and Earth System Sciences 24.12 (2024): 4661-4682.
  7. Nobre, Gabriela Guimarães, et al. “Forecasting, thresholds, and triggers: Towards developing a Forecast-based Financing system for droughts in Mozambique.” Climate Services 30 (2023): 100344.
  8. Vogel, Kristin, et al. “Bayesian network learning for natural hazard analyses.” Natural Hazards and Earth System Sciences 14.9 (2014): 2605-2626.
  9. Russell, Stuart. “Unifying Logic and Probability: A New Dawn for AI?.” Information Processing and Management of Uncertainty in Knowledge-Based Systems: 15th International Conference, IPMU 2014, Montpellier, France, July 15-19, 2014, Proceedings, Part I. Springer International Publishing, 2014.
  10. Parr, Thomas, Giovanni Pezzulo, and Karl J. Friston. Active inference: the free energy principle in mind, brain, and behavior. MIT Press, 2022.
  11. Sillmann, Jana, et al. “Event‐based storylines to address climate risk.” Earth’s Future 9.2 (2021): e2020EF001783.
  12. van den Hurk, Bart JJM, et al. “Climate impact storylines for assessing socio-economic responses to remote events.” Climate Risk Management 40 (2023): 100500.
  13. De Bruijn, K. M., et al. “The storyline approach: a new way to analyse and improve flood event management.” Natural Hazards 81 (2016): 99-121.
  14. Shepherd, Theodore G., et al. “Storylines: an alternative approach to representing uncertainty in physical aspects of climate change.” Climatic Change 151 (2018): 555-571.