Retrieving Differential Kinematics from Networks of Wind and Thermodynamic Profilers

Significant research and engineering has been invested in the remote retrieval of the wind profile, which is especially important for nowcasting of potentially severe convection. While this technology is now instrumental to forecasting, a single wind profiler only retrieves profiles of the vector wind itself; it does not retrieve derivative kinematics such as vorticity, divergence, or advection, which are similarly important pieces of information for forecasting severe convection. While it is possible to retrieve derivative kinematics by finite differencing and gridding, there are numerous drawbacks - in particular, there are significant uncertainties and discontinuities introduced by gridding the data unless the profiler network is particularly dense, or other sources of data are considered for assimilation, vastly increasing computational complexity.

An alternative approach is to apply vector calculus (such as Green's theorem) or spectral analysis (such as Barnes objective analysis). These approaches have been successfully applied to the former NOAA Profiler Network. In particular, Wagner et al. 2022 demonstrated its value when applied to Atmospheric Radiation Measurement Southern Great Plains profiling network and showed that the correlation between finite difference-based retrieval and Green’s theorem-based retrieval is maximized when the distance between profilers is about 50 km - a typical spacing between profilers in meso-alpha field campaigns such as VORTEX-SE and PERiLS.

This presentation explores the application of differential kinematic retrieval to archived data from those field campaigns, each of which involved the deployment of numerous wind profilers in a meso-beta to meso-alpha network. The potential for the use of these retrievals in real-time applications for operational nowcasting is also explored. Different sources of wind profiles are considered to broaden the applicability of the method.