A novel identification and tracking method of weather-relevant 3D Potential vorticity streamers

Potential vorticity streamers (PVSs) are elongated quasi-horizontal filaments of stratospheric air in the upper troposphere related to, for example, Rossby wave breaking events. They are known to be related to partly extreme weather events in the midlatitudes and subtropics and can also be involved in (sub-)tropical cyclogenesis. While several algorithms have been developed to identify and track PVSs on planar isentropic surfaces, less is known about the evolution of these streamers in 3D, both climatologically but also for a better understanding of individual weather events. Furthermore, characteristics of their 3D shape have barely been considered as a predictor for high impact weather events like (sub-)tropical cyclones.

We introduce a novel algorithm for detection and identification of PVSs based on image processing techniques which can be applied to 2D and 3D gridded datasets. The potential vorticity was taken from high resolution isentropic analyses based on the ERA5 dataset. The algorithm uses the 2 PVU (Potential Vorticity Unit) threshold to identify and extract anomalies in the PV field using signed distance functions. This is accomplished by using a stereographic projection to eliminate singularities and keeping track of the reduced distortions by storing precomputed distance maps. This approach is computationally efficient and detects more interesting structures that exhibit the general behavior of PVSs compared to existing 2D techniques.

For each identified object a feature vector is computed, containing the individual characteristics of the streamers. In the 3D case, the algorithm looks at the structure en bloc instead of operating individually on multiple 2D levels. This also makes the identification stable regarding the seasonal cycle. Feature vectors contain parameters about quality, intensity and shape. In the case of 2D datasets, best-fitting ellipses computed from the statistical moments are regarded as a description of their shape. For 3D datasets, recent visualizations show that the boundary of these structures could be approximated by quadric surfaces . The feature vectors are also amended by tracking information, for example splitting and merging events. This low-dimensional representation serves as base for ERA5 climatologies. The data will be correlated with (sub-)tropical cyclone occurrence to spot useful and novel predictors for cyclone activity and preceding Rossby Wave Breaking events.

Overall, this new type of PVS identification algorithm, applicable in 2D or 3D, allows to diagnose the role of PVS in extreme weather events, including their predictability in ensemble forecasts.