Project Details
Projekt
Understanding Large Hail Formation and Trajectories (LIFT)
Subject Area
Atmospheric Science
Term
since 2023
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 522205738
Current methods for radar-based nowcasting of hailstorms assume that the expected further evolution of observed hail is primarily governed by advection; they do not consider ongoing hail growth and production. Combined with the complex internal structure and dynamics of hailstorms, large uncertainties result in the forecasted hail size distribution and area impacted by hail at the ground. To advance current schemes towards a physically-based nowcasting of hail, we propose the Large Hail Formation and Trajectories (LIFT) project in order to generate an observational basis for and derive a first radar-derived hail growth and nowcasting scheme. To develop this capacity, the LIFT project will make use of Swabian MOSES campaign data and also conduct two summer experiments in southern Germany, where comprehensive observations from a dense network meet frequent damaging hailstorms. For the first time, LIFT will exploit and synergistically combine advanced weather radar remote sensing, in-situ instruments, and numerical storm-scale modelling to deliver a comprehensive dataset suitable for reconstructing the evolution of hail growth. LIFT will actively engage citizen scientists to report hailstorms in the WarnWetter App of DWD. The experiment will include innovative new instruments operated in a mobile and flexible setup, including serial Lagrangian radiosondes, aerial drone imagery, and hailstone-trajectory probes. Remotely sensed signatures of hailstorm properties will be carefully evaluated for measurement uncertainty and their sensitivity to environmental conditions using numerical simulations and ground observations. Indicators of hail production and growth will be identified through the combined evaluation of observation and simulation datasets that will form the basis of an observationally-based hail growth model. This multi-parameter hail growth model will be combined with hail trajectory and melting models to identify those processes and their formulation in a physically-based hail nowcasting model that are most beneficial for improving the skill and lead time of radar-derived hail warnings. The LIFT project will deliver a significant step forward for improving the reliability of future hail warning schemes and reducing their uncertainty.
DFG Programme
Research Grants
International Connection
Australia, USA
Cooperation Partners
Professor Matthew R. Kumjian, Ph.D.; Joshua Soderholm, Ph.D.
