Object-based Nowcasting at DWD using KONRAD3D, HYMEC, and Lightning Data

In recent years, a new nowcasting algorithm has been developed at DWD (Deutscher Wetterdienst), called KONRAD3D. It aims to automatically detect, track, and nowcast convective cells in order to support DWD’s warning decisions. The deterministic core of KONRAD3D consists of state of the art techniques, e.g. adaptive thresholding for the cell detection and Kalman filtering of cell centroids and velocities during the tracking step.

Originally, KONRAD3D made use of three-dimensional radar reflectivity data only. Currently, work is in progress to include lightning data and information on hydrometeor types that is based on polarimetric radar data. In this context, we will introduce a new polarimetric hail flag—a parameter that assesses a cell’s threat of hail—that rests upon the hydrometeor data and should roughly estimate the expectable near-ground hail size.

Studies about relationships of lightning and the hail amount of KONRAD3D cells showed strong correlations. A lightning jump detection within KONRAD3D turned out to be a promising approach for hail nowcasting. Statistics on 800 hailstorms over Germany between April and September 2019 revealed that lightning jumps occur 15 to 20 minutes before the maximum near-ground hail intensity on average.

One part of DWD’s project SINFONY (Seamless INtegrated FOrecastiNg sYstem) focuses on extending KONRAD3D towards an object-based ensemble nowcasting algorithm called KONRAD3D-EPS. It enables a suitable way of including cell life-cycle models in order to better predict intensification and weakening tendencies..

We give an overview of our deterministic cell detection and tracking algorithm KONRAD3D and present statistics of cell attributes. Moreover, we demonstrate the concept of our probabilistic nowcasting system. We also illustrate the basic functionalities of our algorithms for prominent example cases with focus on hail threat assessment.