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

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 warning 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. Other features of KONRAD3D are the gust flag – a warning parameter that estimates the maximum speed of wind gusts - and the heavy rain flag which assesses the potential of heavy rain.

This is where the crowdsourcing data comes into play. Observations from app users are able to confirm expected hail sizes on the ground and provide promptly information about wind gusts and rain intensity. Preliminary results show that KONRAD3D tends to overestimate hail and underestimate gusts and heavy rain. Our analyses will show, in which cases the warning parameter estimates were reasonable and at which point the user reports could complement the real-time operation of KONRAD3D.