The Economic Value Of Ensemble-Based Weather Forecasts

TLDR

The economic advantage of ensemble forecasts over control forecasts is examined, yet it remains uncertain whether postprocessed single forecasts can match ensembles in distinguishing predictable cases. The study uses two unpostprocessed forecast systems with only simple calibration and a decision‑making model that classifies users by the ratio of action cost to potential loss. The ensemble forecast system is usable by a broader user base and yields greater economic benefit than a higher‑resolution control forecast beyond four‑day lead times, owing to its richer probability distribution that enables tailored actions and better discrimination of predictability.

Abstract

The potential economic benefit associated with the use of an ensemble of forecasts versus an equivalent or higher-resolution control forecast is discussed. Neither forecast systems are postprocessed, except a simple calibration that is applied to make them reliable. A simple decision-making model is used where all potential users of weather forecasts are characterized by the ratio between the cost of their action to prevent weather-related damages, and the loss that they incur in case they do not protect their operations. It is shown that the ensemble forecast system can be used by a much wider range of users. Furthermore, for many, and for beyond 4-day lead time for all users, the ensemble provides greater potential economic benefit than a control forecast, even if the latter is run at higher horizontal resolution. It is argued that the added benefits derive from 1) the fact that the ensemble provides a more detailed forecast probability distribution, allowing the users to tailor their weather forecast–related actions to their particular cost–loss situation, and 2) the ensemble's ability to differentiate between high-and low-predictability cases. While single forecasts can statistically be supplemented by more detailed probability distributions, it is not clear whether with more sophisticated postprocessing they can identify more and less predictable forecast cases as successfully as ensembles do.