Skill and relative economic value of the ECMWF ensemble prediction system

TLDR

The study evaluates the economic value of the ECMWF ensemble prediction system relative to a perfect deterministic forecast and examines how ensemble size affects that value. Economic value is quantified by comparing EPS probability forecasts to deterministic forecasts from the same model, assessing benefit across users, and analyzing the impact of varying ensemble member counts. EPS delivers substantial medium‑range value, with probability forecasts outperforming deterministic ones, offering benefits equivalent to years of model development, and larger ensembles (e.g., 50 members) provide additional user value beyond small ensembles.

Abstract

Abstract The economic value of the European Centre for Medium‐Range Weather Forecasts (ECMWF) operational ensemble prediction system (EPS) is assessed relative to the value of a perfect deterministic forecast. The EPS has substantial relative value throughout the medium range. Probability forecasts derived from the EPS are of greater benefit than a deterministic forecast produced by the same model. Indeed, for many users, the probability forecasts have more value than a shorter‐range deterministic forecast. Based on the measures used here, the additional information in the EPS (reflecting the uncertainty in the initial conditions) provides a benefit to users equivalent to many years' development of the forecast model and assimilation system. The impact of ensemble size on forecast value is considered. The difference in performance between ensembles with 10 and with 50 members may appear relatively small, based on standard skill measures, yet the larger ensembles have substantial benefit to a range of users. Further increases in ensemble size may be expected to provide additional value.