In the world of seasonal to subseasonal (S2S) weather forecasting, understanding the delicate balance between preemptive action and potential loss is crucial. Navigating the complexities of weather-related risks with confidence isn’t easy – probabilistic decision-making is critical for business leaders but can be difficult to master.

Here we explore the basics of the cost-loss decision model, a fundamental principle that guides business leaders’ decisions by balancing the costs of preventative measures against the potential losses from weather events.

‍Understanding the Cost-Loss Model

The cost-loss model is a decision-making tool that assesses the trade-offs between the cost of taking preventive action (C) and the potential loss (L) should a weather event occur without mitigation. This framework helps answer a critical question: "Is the cost of taking action less than the expected loss from the event, considering its probability (P)?"

The matrix of costs is:

The expected cost of taking the preventative action is C; the expected cost of not taking the preventative action is P*L. The preventative action is the right decision in a cost-benefit analysis if C < P*L and is the wrong decision if C > P*L. The threshold probability above which it makes sense to take preventative action is C/L. This is the cost-loss ratio. 

A critical challenge of using the cost-loss decision model, and indeed a significant consideration for any predictive framework, is the inherent assumption that decision-makers have numerous opportunities to apply these decisions, akin to being the 'house' in a casino scenario. This perspective assumes a long-term ability to absorb variability and occasional losses.

As Salient climate scientist Brian Zimmerman drives home – both internally at Salient and externally, see his recent presentation at American Geophysical Union (poster) – even if one operates at the optimal probability threshold where preventive action costs align with potential losses (P=C/L), the risk of consecutive losses is far from trivial, though the odds decrease when using a reliable forecast over an unreliable forecast.

This underscores a vital aspect of risk management: the capacity to endure multiple adverse outcomes in succession. This reality necessitates a robust financial strategy that not only considers the statistical probabilities of weather-related events but also the financial resilience to withstand periods of repeated losses. 

Understanding this dynamic is crucial for effectively leveraging the cost-loss decision model to increase ROI in the long term – a comprehensive approach to risk that accounts for the potential for consecutive years of unfavorable outcomes is required. 

Let’s look at some real-world examples because, for businesses in energy and agriculture, this model is not just theoretical. Here's how the cost-loss framework applies to these sectors.

Energy

Energy companies face the challenge of balancing supply and demand, especially with the rise of renewable energy sources like wind and solar power, which are directly influenced by weather conditions. Ivan Penn’s recent piece in The New York Times on the electrical grid stress caused by summer and winter peak demands illustrates this nicely. Using the cost-loss model, utilities and energy companies can determine when plant maintenance, vegetation management, crew placement, gas procurement, and more has the highest likelihood to be cost-effective based on Salient’s probabilistic S2S weather forecasts. 

This not only has the capability to increase reliability and efficiency in energy supply, but also optimizes financial planning and risk management for an industry deeply affected by extreme heat and cold, flooding and drought, wind and sunshine, and snow, ice, and wildfires.

Agriculture

For the agriculture sector, the cost-loss framework aids in making informed decisions in an era of volatile weather about planting, irrigation, nutrient application, harvesting and more. Weather causes the most variability in crop production – more than soil, land, farmer skill, or plant genetics. With accurate, reliable, calibrated S2S forecasts:

• Farmers can be more confident in taking actions such as purchasing drought-resistant seeds, or early application of fertilizer and pest-control products to protect against potential crop failure due to drought or floods or extreme temperatures, 
• Suppliers that provide inputs – seeds, nutrients, and crop-protection products among them – can make more informed decisions regarding manufacturing and then placing products on shelves around the globe, and
• Food and beverage producers that depend on crop yields to meet projections can evaluate the costs of hedging and forward contracting decisions. Related: Dive deeper into Salient’s partnership with AB InBev to understand the business opportunities S2S forecasts unlock.

If consistently applied over time, this approach to informed decision-making minimizes risks and enhances profitability (to the tune of millions of dollars).

The Role of S2S Forecasts

At Salient, our S2S weather forecasts play a vital role in the cost-loss framework by providing the probability (P) of weather events. Our proprietary model incorporates advanced machine learning and AI, setting us apart from competitors who rely solely on postprocessed government data. For more on this, read Salient data scientist Fran Bartolic on why ML offers new possibilities in S2S forecasting, hear chief scientist Sam Levang discuss how good weather forecasting can get with The Economist, and listen to climate scientist Brian Zimmerman explain the attention to detail that honest S2S forecasts require (minute 6:02). 

Conclusion

The cost-loss model is an essential tool for any business affected by weather-related risks. By leveraging accurate and reliable S2S weather forecasts, companies in the energy and agriculture sectors can make informed decisions that optimally balance the cost of preventative actions against potential losses. In an ever-changing climate, this framework is not just about managing risk—it's about seizing opportunity and turning forecasts into foresight.

PS: Ready for Cost-Loss Model 201? 

Here are a few of our favorite scientific papers at the intersection of weather forecasting and this decision-making framework:

1. Richardson, David S. “Applications of Cost-Loss Models.” Proc. Seventh ECMWF Workshop on Meteorological Operational Systems, Reading, United Kingdom, ECMWF, 2000, pages 209–213.
2. Jewson, Stephen, et al. “Decide Now or Wait for the Next Forecast? Testing a Decision Framework Using Real Forecasts and Observations.” Monthly Weather Review, Volume 149, Issue 6, May 4, 2021, pages 1237-1650.
3. Richardson, David S. "Predictability and Economic Value." Predictability of weather and climate, 628, ECMWF, 2006, pages 321 - 332.

And for an even deeper dive, Salient climate scientist Brian Zimmerman’s presentation for US CLIVAR on better data practices in S2S forecasting emphasizes the care the Salient team takes to provide honest assessments of the skill and reliability of our models. Watch the 25-minute presentation here.