Neighborhood Ensemble Copula Coupling: Smoother and Sharper Calibrated Ensembles

Abstract

Abstract Ensemble copula coupling (Schefzik et al.) is a widely used method to produce a calibrated ensemble from a calibrated probabilistic forecast. This process improves the statistical accuracy of the ensemble; in other words, the distribution of the calibrated ensemble members at each grid point more closely approximates the true expected distribution. However, the trade-off is that the individual members are often less physically realistic than the original ensemble: there is noisy variation among neighboring grid points, and, depending on the calibration method, extremes in the original ensemble are sometimes muted. We introduce neighborhood ensemble copula coupling (N-ECC), a simple modification of ECC designed to mitigate these problems. We show that, when used with the calibrated forecasts produced by Flowerdew’s (Flowerdew) reliability calibration, N-ECC improves both the visual plausibility and the statistical properties of the forecast. Significance Statement Numerical weather prediction (NWP) uses physical models of the atmosphere to produce a set of scenarios (called an ensemble) describing possible weather outcomes. These forecasts are used in other models to produce weather forecasts and warnings of extreme events. For example, NWP forecasts of rainfall are used in hydrological models to predict the probability of flooding. However, the raw NWP forecasts require statistical postprocessing to ensure that the range of scenarios they describe accurately represents the true range of possible outcomes. This paper introduces a new method of processing NWP forecasts to produce physically realistic, well-calibrated ensembles.