Members in ensemble forecasts differ due to the representations of initial uncertainties and model uncertainties. The inclusion of stochastic schemes to represent model uncertainties has improved the probabilistic skill of the ECMWF ensemble by increasing reliability and reducing the error of the ensemble mean. Recent progress, challenges and future directions regarding stochastic representations of model uncertainties at ECMWF are described in this article. The coming years are likely to see a further increase in the use of ensemble methods in forecasts and assimilation. This will put increasing demands on the methods used to perturb the forecast model. An area that is receiving greater attention than 5–10 years ago is the physical consistency of the perturbations. Other areas where future efforts will be directed are the expansion of uncertainty representations to the dynamical core and other components of the Earth system, as well as the overall computational efficiency of representing model uncertainty.

Synoptic-scale error growth near the tropopause is investigated from a process-based perspective. Following previous work, a potential vorticity (PV) error tendency equation is derived and partitioned into individual contributions to yield insight into the processes governing error growth near the tropopause. Importantly, we focus here on the further amplification of preexisting errors and not on the origin of errors. The individual contributions to error growth are quantified in a case study of a 6-day forecast. In this case, localized mesoscale error maxima have formed by forecast day 2. These maxima organize into a wavelike pattern and reach the Rossby wave scale around forecast day 6. Error growth occurs most prominently within the Atlantic and Pacific Rossby wave patterns. In our PV framework, the error growth is dominated by the contribution of upper-level, near-tropopause PV anomalies (near-tropopause dynamics). Significant contributions from upper-tropospheric divergent flow (prominently associated with latent heat release below) and lower-tropospheric anomalies [tropospheric-deep (i.e., baroclinic) interaction] are associated with a misrepresentation of the surface cyclone development in the forecast. These contributions are, in general, of smaller importance to error growth than near-tropopause dynamics. This result indicates that the mesoscale errors generated near the tropopause do not primarily project on differences in the subsequent baroclinic growth, but instead directly project on the tropopause evolution and amplify because of differences in the nonlinear Rossby wave dynamics.

The standard, strong‐constraint formulation of 4D‐Var is designed to correct for random, zero‐mean errors from the model forecast and the observations. However, significant systematic errors are generated by the forecast models used in global numerical weather prediction (NWP), and the Integrated Forecast System (IFS) model of the European Centre for Medium‐Range Weather Forecasts (ECMWF) is no exception. To deal with this type of error, a modification of the standard 4D‐Var algorithm, weak‐constraint 4D‐Var, has long been proposed and was implemented in operations at ECMWF in 2009. In the original implementation, the model error corrections were only active in the upper stratosphere, as a full application of the algorithm would lead to unacceptable degradation of forecast scores. Thus the impact of weak‐constraint 4D‐Var on ECMWF analyses and forecasts has always been small heretofore.In a companion article, it was shown through theoretical considerations and experiments with a simplified model that weak‐constraint 4D‐Var can correct accurately for model errors and initial‐state errors only when background and model errors have different spatial scales and when the observing system has a component, sufficient in number, that is unbiased and spatially homogeneous. This article presents new diagnostics based on radio occultation (RO) temperature retrievals and shows how those assumptions are verified in the IFS model. A new model‐error covariance matrix is derived and reduces the analysis and first‐guess temperature bias in the stratosphere by up to 50%. We also present how the model error tendencies diagnosed by weak‐constraint 4D‐Var can be used to reduce mean errors in medium‐range forecasts and discuss how they can also be applied in a reanalysis context to improve the fidelity and continuity of climate reconstructions.

Earth’s climate is a nonlinear dynamical system with scale-dependent Lyapunov exponents. As such, an important theoretical question for modeling weather and climate is how much real information is carried in a model’s physical variables as a function of scale and variable type. Answering this question is of crucial practical importance given that the development of weather and climate models is strongly constrained by available supercomputer power. As a starting point for answering this question, the impact of limiting almost all real-number variables in the forecasting mode of ECMWF Integrated Forecast System (IFS) from 64 to 32 bits is investigated. Results for annual integrations and medium-range ensemble forecasts indicate no noticeable reduction in accuracy, and an average gain in computational efficiency by approximately 40%. This study provides the motivation for more scale-selective reductions in numerical precision.

Tropical cyclones are some of the most devastating natural hazards and the “three beasts”—Harvey, Irma, and Maria—during the Atlantic hurricane season 2017 are recent examples. The European Centre for Medium-Range Weather Forecasts (ECMWF) is working on fulfilling its 2016–25 strategy in which early warnings for extreme events will be made possible by a high-resolution Earth system ensemble forecasting system. Several verification reports acknowledge deterministic and probabilistic tropical cyclone tracks from ECMWF as world leading. However, producing reliable intensity forecasts is still a difficult task for the ECMWF global forecasting model, especially regarding maximum wind speed. This article will put the ECMWF strategy into a tropical cyclone perspective and highlight some key research activities, using Harvey, Irma, and Maria as examples. We describe the observation usage around tropical cyclones in data assimilation and give examples of their impact. From a model perspective, we show the impact of running at 5-km resolution and also the impact of applying ocean coupling. Finally, we discuss the future challenges to tackle the errors in intensity forecasts for tropical cyclones.