Projects during Phase 2 (2019-2023)
Projects in Research Area A – Upscale Error Growth
Coordinator: Michael Riemer
Local processes, such as cumulus convection or flow over orography, create disturbances in the atmospheric flow, but most are unimportant for the future development of the weather. In order to have an influence over several days and many thousands of kilometers, the disturbances need to project onto the synoptic scale, geostrophically balanced modes that dominate the midlatitude atmosphere. To understand and model this chain of events requires progress in the physical problem of the mechanisms for upscale growth, the statistical or stochastic problem of modeling the unpredictable, small-scale processes that trigger disturbance growth, and the data analysis problem of how to represent the changing uncertainty represented by an ensemble of forecasts.
- A1 – Multiscale analysis of the evolution of forecast uncertainty
- A2 – Impact of structured heat sources on larger scales in atmospheric dynamics
- A3 – Model error and uncertainty at the midlatitude tropopause
- A6 – Representing the evolution of forecast uncertainty
- A7 – Visualization of coherence and variation in meteorological dynamics
- A8 – Dynamics and predictability of blocked regimes in the Atlantic-European region
Projects in Research Area B – Cloud-scale Uncertainties
Coordinator: Christian Keil
Clouds are a major contributor to uncertainty in weather prediction for several reasons. They are composed of microphysical particles that grow, dissipate and interact in complex processes. Cloud particles interact with the atmospheric flow through release and absorption of latent heat, through precipitation, and through radiative feedbacks and mixing. This can give rise to complex dynamical structure on scales as small as tens of meters, but also to impacts on growing weather systems such as convective clouds and synoptic cyclones. A further source of complexity is that clouds are sensitive to environmental factors such as temperature, humidity, transport and aerosol content. These different sources of uncertainty must be investigated in detail in order to represent their effects accurately in weather prediction systems.
- B1 – Microphysical uncertainties in hailstorms using statistical emulation and stochastic cloud physics
- B3 – Sources of uncertainty for convective-scale predictability
- B4 – Radiative interactions at the NWP scale and their impact on midlatitude cyclone predictability
- B5 – Data-driven analysis and learning of the temporal evolution of ensemble forecasts
- B6 – New data assimilation approaches to better predict tropical convection
- B7 – Identification of robust cloud and precipitation states via inverse methods
- B8 – Role of uncertainty in ice microphysical processes in warm conveyor belts
Projects in Research Area C – Predictability of local Weather
Coordinator: Andreas Fink
The prediction of a local weather event is affected by the synoptic-scale environment and by local processes and instabilities. The uncertainty of a forecast will be determined by a combination of these factors. However, the processes contributing to this uncertainty are as diverse as the weather systems themselves. An investigation of the predictability of local weather must consider a wide variety of dynamical processes and weather events, to achieve the final goal of a probabilistic forecast that is useful for society.
- C2 – Statistical-dynamical forecasts of tropical rainfall
- C3 – Predictability of tropical and hybrid cyclones over the North Atlantic Ocean
- C4 – Predictability of European heat waves
- C5 – Dynamical feature-based ensemble postprocessing of wind gusts within European winter storms
- C8 – Stratospheric influence on predictability of persistent weather patterns
- C9 – Visual feature analysis from individual cases to collections of ensembles
- T1 – Development of a predictability index for severe weather events over Europe (transfer project)
- T2 – Towards seamless prediction of extremes (transfer project)
- T4 – Development of a deep learning prototype for operational probabilistic wind gust forecasting (transfer project)
