4 papers
Physical Scales Matter: The Role of Receptive Fields and Advection in Satellite-Based Thunderstorm Nowcasting with Convolutional Neural Networks
Christoph Metzl, Kianusch Vahid Yousefnia, Richard Müller +3
The focus of nowcasting development is transitioning from physically motivated advection methods to purely data-driven Machine Learning (ML) approaches. Nevertheless, recent work i…
Inferring Thunderstorm Occurrence from Vertical Profiles of Convection-Permitting Simulations: Physical Insights from a Physical Deep Learning Model
Kianusch Vahid Yousefnia, Christoph Metzl, Tobias Bölle
Thunderstorms have significant social and economic impacts due to heavy precipitation, hail, lightning, and strong winds, necessitating reliable forecasts. Thunderstorm forecasts b…
Increasing NWP Thunderstorm Predictability Using Ensemble Data and Machine Learning
Kianusch Vahid Yousefnia, Tobias Bölle, Christoph Metzl
While numerical weather prediction (NWP) models are essential for forecasting thunderstorms hours in advance, NWP uncertainty, which increases with lead time, limits the predictabi…
A machine-learning approach to thunderstorm forecasting through post-processing of simulation data
Kianusch Vahid Yousefnia, Tobias Bölle, Isabella Zöbisch +1
Thunderstorms pose a major hazard to society and economy, which calls for reliable thunderstorm forecasts. In this work, we introduce a Signature-based Approach of identifying Ligh…