ECSS2023-54
https://doi.org/10.5194/ecss2023-54
11th European Conference on Severe Storms
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

DWD-Crowdsourcing: Are User Reports beneficial for Object-based Nowcasting?

Arne Spitzer, Harald Kempf, Matthias Jerg, Manuel Werner, and Ulrich Blahak
Arne Spitzer et al.
  • Deutscher Wetterdienst, Offenbach am Main, Germany (arne.spitzer@dwd.de)

Since July 2020 the DWD WarnWetter-App comprises the Crowdsourcing module “User Reports”. This module provides users the functionality to report observations about current weather conditions and severe weather to DWD and other users.

The user reports represent the current meteorological conditions at a certain place at a certain point of time. The Crowdsourcing module provides 10 different meteorological categories (lightning, wind, hail, rain, wet icy conditions, snowfall, snow cover, cloudiness, fog, tornado), each of which contains specific characteristic levels and optionally additional attributes. In addition, the user has the option of setting the location and time of the event manually.

The benefit of the data is that meteorological information at ground level is collected at places where no weather station is located in the immediate vicinity. The dataset is able to complement the existing synoptic station network. Forecasters from DWD already benefit from user-based observations that are available in near real-time.

In recent years, a new nowcasting algorithm has been developed at DWD, called KONRAD3D. The algorithm aims to automatically detect, track, and nowcast convective cells in order to support DWD’s warning management.

KONRAD3D uses three-dimensional radar reflectivity data as main input. In addition, also lightning data and information about hydrometeor types based on polarimetric radar data is regarded. In particular, in the latest version KONRAD3D features the new hail flag - a parameter that assesses a cell’s threat of hail. The new parameter rests upon the hydrometeor data and should roughly estimate the expectable near-ground hail size.

This is where the crowdsourcing data comes into play. Hail reports from app users are able to confirm expected hail sizes on the ground. Our analyses will show, whether the hail threat estimates were reasonable and at which point the user reports could complement the real-time operation of KONRAD3D.

How to cite: Spitzer, A., Kempf, H., Jerg, M., Werner, M., and Blahak, U.: DWD-Crowdsourcing: Are User Reports beneficial for Object-based Nowcasting?, 11th European Conference on Severe Storms, Bucharest, Romania, 8–12 May 2023, ECSS2023-54, https://doi.org/10.5194/ecss2023-54, 2023.