Evaluating Weather Forecasts from a Decision Maker's Perspective
This addresses the gap in weather forecast evaluation for decision-makers, offering a novel framework that is incremental in shifting focus from statistical metrics to practical utility.
The paper tackled the problem of evaluating weather forecasts from a decision-maker's perspective rather than a statistical one, finding that forecast-level performance does not reliably translate to decision-making improvements, with model rankings changing across different tasks.
Standard weather forecast evaluations focus on the forecaster's perspective and on a statistical assessment comparing forecasts and observations. In practice, however, forecasts are used to make decisions, so it seems natural to take the decision-maker's perspective and quantify the value of a forecast by its ability to improve decision-making. Decision calibration provides a novel framework for evaluating forecast performance at the decision level rather than the forecast level. We evaluate decision calibration to compare Machine Learning and classical numerical weather prediction models on various weather-dependent decision tasks. We find that model performance at the forecast level does not reliably translate to performance in downstream decision-making: some performance differences only become apparent at the decision level, and model rankings can change among different decision tasks. Our results confirm that typical forecast evaluations are insufficient for selecting the optimal forecast model for a specific decision task.