Polarimetric Radar Signatures of ZDR and KDP in Tornadic Storms over Germany and their Use for Nowcasting
Bibliographic record
Abstract
Germany has one of the highest densities of tornado reports in Europe with several damaging tornadoes observed every year. Although the German Meteorological Service (DWD) has a dedicated warning process in place, it relies on voluntary observers on the ground to confirm tornadoes. Thus, a robust and reliable radar-based detection algorithm for tornadic cells would represent a great improvement of the warning process. Previous studies by Loeffler and Kumjian (2018, Weather&Forecasting, 33(5), 1143-1157) and Loeffler et al. (2020, GRL,47(12), e2020GL088242) showed pathways to distinguish between tornadic and non-tornadic supercells based on signatures of differential reflectivity (ZDR) and specific differential phase (KDP) in polarimetric radar observations. Storm relative winds cause size sorting in precipitation, leading to a higher concentration of larger drops at the forward flank of a supercell and a localized maximum of ZDR. Smaller drops are advected further into the core of the convective cell, causing enhanced values of KDP. In most storms, ZDR and KDP signatures are spatially separated along the storm motion, but in tornadic supercells, and in some tornadic non-supercells, this separation tends to be more perpendicular to the motion vector. This study uses measurements of DWD's polarimetric C-Band radar network to investigate the separation signature in tornadic storms over Germany and its potential for nowcasting. This data is available since 2021 with a radial resolution of 250 m. The analysis includes 16 tornado cases observed in 2021 and 2022, including supercell and non-supercell tornadoes. An clustering algorithm and percentile-defined thresholds are exploited to identify and analyze the separation signature. Results confirm the existence of the signature in both cell types, showing more consistent separations in the supercell cases showing a good potential for nowcasting with lead times of 5 − 20 min. For non-supercell tornadoes, however, the value of the signatures is limited and can at best confirm the occurrence of a tornado. Results also show differing track characteristics of clusters with enhanced ZDR and KDP for both cell types. In supercells, the clusters tend to deviate less around the linear direction of storm motion, which may be linked to the degree of organization of the storm. An extended and revised version of the algorithm is assumed to significantly improve the warning process for tornadoes in Germany.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".