Select a Decision Scenario
Choose a use case to see how forecast accuracy translates into economic value for real-world decisions.
Electric Utility
Grid ops protecting against weather-driven demand spikes
Event Planner
Outdoor events with multi-day advance planning
Rocket Launch
Launch window assessment with high-confidence requirements
Custom
Configure your own costs and variables
Bottom Line: Estimated Annual Savings
Customize Parameters
Cost Matrix
Variable Weights
Economic Value Across Lead Times
How does the economic value of each model's forecast change as prediction horizon extends? The shaded region shows your selected lead-time window. Models that maintain value at longer lead times are more useful for advance planning.
Dollar Impact at Key Milestones
A side-by-side comparison of per-decision dollar value at the five key forecast milestones (6h, 24h, 48h, 72h, 114h), filtered to your selected lead-time range.
Cost-Loss Ratio Sensitivity
The classic Richardson diagram shows how forecast value varies with the cost-loss ratio (protection cost ÷ miss cost). The dashed vertical line marks your current scenario. Forecasts are most valuable when events are rare but costly (low C/L ratio).
Model Economic Summary
Per-model breakdown showing break-even lead time (latest lead time with positive value), peak value lead time, and annualized savings estimate.
Methodology & Caveats
Approach
This tool uses the Richardson Economic Value framework to translate forecast accuracy metrics into approximate dollar estimates. The core approach:
- Quality Factor: For each model, variable, and lead time, we compute Q = clamp(1 − RMSEmodel / RMSEbaseline, 0, 1), where the baseline is the 4DWX operational model.
- Cost-Loss Model: A decision-maker either protects (paying protection cost) or takes no action (risking the miss cost if an event occurs). The value of a forecast is the fraction Q of maximum possible savings the forecast captures.
- Weighted Aggregation: Quality factors are weighted across user-selected variables, then averaged across the selected lead-time range.
- Annualization: Per-decision value is multiplied by 1,460 decision cycles per year (one every 6 hours).
Data Source
RMSE values computed using the METplus verification framework against observations from surface stations across southern New Mexico (Domain d03, 3.3 km resolution). Lead times: 6h to 114h in 6-hour increments.
Important Caveat: These dollar figures are simplified estimates designed to illustrate the relative economic benefit of improved forecast accuracy. They are not financial guarantees. The underlying quality factor is derived from RMSE ratios, not from true event-based contingency tables. Actual economic impact depends on many factors not captured here, including decision timing, spatial specificity, event definition thresholds, and organizational response capacity. Use these figures for comparative insight, not for budget planning.