This research demonstrates improved wind retrievals in hailstorm analysis, indicating advancements in numerical weather prediction and severe weather forecasting.
Understanding the life cycle of hailstorms is crucial for improving numerical weather prediction (NWP) models to accurately forecast severe weather. As part of the project Understanding Large Hail Formation and Trajectories (LIFT), the primary goal of this study is to improve our understanding of large hail formation and hail trajectory models (e.g. HailTrack; Brooks et al., 2021), towards a more accurate prediction of severe hail events. The study employs both the operational C-band radar network of the German Weather Service (DWD) and data collected during the In-situ Collaborative Experiment for Collection of Hail In the Plains (ICECHIP; Adams-Selin et al., 2024) field campaign. The open-source algorithm Pythonic Direct Data Assimilation (PyDDA) is employed for the wind retrievals. PyDDA uses the 3D variational method (3DVAR), minimising the sum of various cost functions depending on radar-measured Doppler winds and atmospheric dynamic conditions (e.g. the continuity equation) and optional other parameters like e.g. radiosoundings. However, minimising the total cost function presents various challenges, e.g. the final wind retrieval depends on adaptable weights of cost function components . Thus, a sensitivity analysis utilising regional convective-allowing simulations of storms in southern Germany obtained from the ICON Rapid Update Cycle (ICON-RUC, hourly updates), part of the DWD's new Seamless INtegrated FOrecastiNg sYstem (SINFONY), is presented. Consistent simulated 3D wind fields are confronted with retrievals obtained from the corresponding forward-simulated (synthetic) observations to optimize the weighting factors. In the final step, the resulting PyDDA-derived 3D wind fields are comparatively analysed with typical radar process signatures of thunderstorms (e.g. columns with enhanced differential reflectivity ZDR), as well as with trajectories from hailsondes. These miniaturised radiosondes are designed to mimic the trajectories of hailstones providing useful insights into a thunderstorms updraft characteristics (e.g. updraft velocities and region) and their further development.
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Scharbach et al. (2025) studied this question.
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