Projects during Phase 1 (2015-2019)
Projects in Research Area A – Upscale Error Growth
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 – Upscale impact of diabatic processes from convective to near-hemispheric scale
A2 – Structure formation on cloud scale and impact on larger scales
A3 – Model error and uncertainty for midlatitude cyclones analyzed using campaign data
A4 – Evolution and predictability of storm structure during extratropical transition of tropical cyclones
A5 – The role of soil moisture and surface- and subsurface water flows on predictability of convection
A6 – Representing forecast uncertainty using stochastic physical parameterizations
A7 – Visualization of coherence and variation in meteorological dynamics
Projects in Research Area B – Cloud-scale Uncertainties
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 deep convective clouds and their implications for data assimilation
B3 – Relative impact of surface and aerosol heterogeneities on the initiation of deep convection
B4 – Radiative heating and cooling at cloud scale and its impact on dynamics
B5 – Data-driven ensemble visualization
B6 – Parameter estimation using a data assimilation system for improved representation of clouds
B7 – Identification of robust cloud patterns via inverse methods
Projects in Research Area C – Predictability of local Weather
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 – Prediction of wet and dry periods of the West African monsoon
C3 – Multi-scale dynamics and predictability of Atlantic Subtropical Cyclones and Medicanes
C4 – Coupling of planetary-scale Rossby wave trains to local extremes in heat waves over Europe
C5 – Forecast uncertainty for peak surface gusts associated with European cold-season cyclones
C7 – Statistical postprocessing and stochastic physics for ensemble predictions
T1 – Development of a predictability index for severe weather events over Europe (transfer project)
Central projects Z
To learn more about the cross-cutting activities "Campaign Data", "Ensemble Tools", and "Visualization", please click here.