Sample Stratification in Verification of Ensemble Forecasts of Continuous Scalar Variables: Potential Benefits and Pitfalls

Abstract

In the verification field, stratification is the process of dividing the sample of forecast–observation pairs into quasi-homogeneous subsets, in order to learn more on how forecasts behave under specific conditions. A general framework for stratification is presented for the case of ensemble forecasts of continuous scalar variables. Distinction is made between forecast-based, observation-based, and external-based stratification, depending on the criterion on which the sample is stratified. The formalism is applied to two widely used verification measures: the continuous ranked probability score (CRPS) and the rank histogram. For both, new graphical representations that synthesize the added information are proposed. Based on the definition of calibration, it is shown that the rank histogram should be used within a forecast-based stratification, while an observation-based stratification leads to significantly nonflat histograms for calibrated forecasts. Nevertheless, as previous studies have warned, statistical artifacts created by a forecast-based stratification may still occur, thus a graphical test to detect them is suggested. To illustrate potential insights about forecast behavior that can be gained from stratification, a numerical example with two different datasets of mean areal precipitation forecasts is presented.