Nowcasting applications of remote sensing to reduce weather risks to traffic.

In recent years, DWD has developed a ground-breaking nowcasting methods for thunderstorms (TS) and strong convection based on intelligent combination of lightning data, satellite information and Numerical Weather Prediction (NWP). The respective NowCastSat-Aviation (NCS-A) method covers the complete geostationary ring and is used in cockpits of many airlines around the world in order to reduce risks of injury by thunderstorms and associated turbulences. However, NCS-A covers only forecasts up to 2 hours.  Thus, recent developments focus on the extension of the forecast horizon by deep learning and combination with an ensemble analysis of the Lightning Potential Index (LPI), provided by the DWD NWP model ICON. Both approaches attempt to overcome the limitations associated with the pure extrapolation of  objects with Atmospheric Motion Vectors (AMVs).  Although blending with NWP enables an improvement of the forecasting capability for predictions up to 3 hours, the CSI drops below 0.5 afterwards. This results from the chaotic nature of thunderstorms, which makes it difficult to model TS accurately. Within this scope it is discussed whether Artificial Intelligence will be able to replace numerical weather modeling in the near future. In fact, the University of Mainz has shown in a joint project that neural networks can learn something about the lifecycle and thus the physics of thunderstorms, leading to an improvement of CSI compared to classical nowcasting.

The talk presentation will start with an overview of the current 24/7 thunderstorm nowcasting. This is followed by a presentation and discussion of the current developments at DWD aimed at providing accurate forecasts of thunderstorms up to 6 hours, including a discussion of NWP versus AI. The presentation will close with overview about the status and the further plans concerning volcanic ash and turbulence nowcasting, both danerous for traffic as well.

References:

Barleben, A.; Haussler, S.; Müller, R.; Jerg, M. A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation. Remote Sens. 2020, 12, 2255. https://doi.org/10.3390/rs12142255 

Müller, R.; Barleben, A.; Haussler, S.; Jerg, M. A Novel Approach for the Global Detection and Nowcasting of Deep Convection and Thunderstorms. Remote Sens. 2022, 14, 3372. https://doi.org/10.3390/rs14143372 4936-

Brodehl, S.; Müller, R.; Schömer, E.; Spichtinger, P.; Wand, M. End-to-End Prediction of Lightning Events from Geostationary Satellite Images. Remote Sens. 2022, 14, 3760. https://doi.org/10.3390/rs14153760 

Müller, R.; Barleben, A. Data Driven Prediction of Severe Convection at DWD. An Overview of Recent Developments. Preprints 2024, 2024031179. https://doi.org/10.20944/preprints202403.1179.v1. In the meanwhile accepted for publication in Atmosphere.